PATENT DOCUMENT

Publication Number: US-9934775-B2
Application Number: US-201615266930-A
Country: US
Kind Code: B2

Title: Unit-selection text-to-speech synthesis based on predicted concatenation parameters

Abstract:
Systems and processes for performing unit-selection text-to-speech synthesis are provided. In an example process, text to be converted to speech is received. The text is represented as a sequence of target units. A plurality of candidate speech segments corresponding to the sequence of target units are selected. Predicted statistical parameters of acoustic features associated with the sequence of target units are determined. The predicted statistical parameters of acoustic features are used to determine target costs and concatenation costs associated with the plurality of candidate speech segments. Based on a combined cost determined from the target costs and concatenation costs, a subset of candidate speech segments is selected from the plurality of candidate speech segments. Speech corresponding to the received text is generated using the subset of candidate speech segments.

Claims:
What is claimed is: 
     
       1. A system for unit-selection text-to-speech synthesis, the system comprising:
 one or more processors; and 
 memory storing one or more programs, wherein the one or more programs include instructions which, when executed by the one or more processors, cause the one or more processors to:
 receive text to be converted to speech; 
 generate a sequence of target units representing a spoken pronunciation of the text; determine, based on a plurality of linguistic features associated with each target unit of the sequence of target units, predicted statistical parameters for each of a plurality of acoustic features associated with each target unit, wherein a second acoustic feature of the plurality of acoustic features represents a change of a first acoustic feature of the plurality of acoustic features across a portion of a respective target unit of the sequence of target units; 
 select, based on the plurality of linguistic features associated with each target unit, a plurality of candidate speech segments corresponding to the sequence of target units; 
 for each candidate speech segment of the plurality of candidate speech segments:
 determine a target cost based on the predicted statistical parameters of the first acoustic feature associated with a respective target unit of the sequence of target units; and 
 determine a plurality of concatenation costs with respect to a plurality of subsequent candidate speech segments, the plurality of concatenation costs determined based on the predicted statistical parameters of the second acoustic feature associated with the respective target unit of the sequence of target units; 
 
 select from the plurality of candidate speech segments a subset of candidate speech segments for speech synthesis, the selecting based on a combined cost associated with the subset of candidate speech segments, wherein the combined cost is determined based on the target cost and the plurality of concatenation costs of each candidate speech segment; and 
 generate speech corresponding to the received text using the subset of candidate speech segments. 
 
 
     
     
       2. The system of  claim 1 , wherein the portion of the respective target unit is an end portion of the respective target unit. 
     
     
       3. The system of  claim 1 , wherein the first acoustic feature comprises a fundamental frequency and the second acoustic feature comprises a change in the fundamental frequency across an end portion of the respective target unit. 
     
     
       4. The system of  claim 1 , wherein the first acoustic feature comprises a mel-frequency cepstral coefficient and the second acoustic feature comprises a change in the mel-frequency cepstral coefficient across an end portion of the respective target unit. 
     
     
       5. The system of  claim 1 , wherein the plurality of acoustic features include a fundamental frequency at the portion of the respective target unit and a fundamental frequency at a second portion of the respective target unit. 
     
     
       6. The system of  claim 1 , wherein the plurality of acoustic features includes a first plurality of mel-frequency cepstral coefficients at the portion of the respective target unit and a second plurality of mel-frequency cepstral coefficients at a second portion of the respective target unit. 
     
     
       7. The system of  claim 1 , wherein the plurality of acoustic features includes a duration of the respective target unit. 
     
     
       8. The system of  claim 1 , wherein the predicted statistical parameters of the second acoustic feature is not derived from the predicted statistical parameters of the first acoustic feature. 
     
     
       9. The system of  claim 1 , wherein the predicted statistical parameters for each of the plurality of acoustic features include a mean parameter for each of the plurality of acoustic features and a variance parameter for each of the plurality of acoustic features. 
     
     
       10. The system of  claim 1 , wherein the target cost for a respective candidate speech segment is based on a weighted difference between an actual value of the first acoustic feature for the respective candidate speech segment and a first predicted statistical parameter of the predicted statistical parameters of the first acoustic feature for the respective target unit, and wherein the weighted difference is weighted by a second predicted statistical parameter of the predicted statistical parameters of the first acoustic feature for the respective target unit. 
     
     
       11. The system of  claim 1 , wherein a concatenation cost of the plurality of concatenation costs for a respective candidate speech segment includes a second weighted difference between an actual value of the second acoustic feature for the respective candidate speech segment with respect to a subsequent candidate speech segment of the plurality of subsequent candidate speech segments and a first predicted statistical parameter of the predicted statistical parameters of the second acoustic feature for the respective target unit, and wherein the second weighted difference is weighted by a second predicted statistical parameter of the predicted statistical parameters of the second acoustic feature for the respective target unit. 
     
     
       12. The system of  claim 11 , wherein the actual value of the second acoustic feature for the respective candidate speech segment with respect to the subsequent candidate speech segment of the plurality of subsequent candidate speech segments comprises a difference between an actual value of the first acoustic feature at an end of the respective candidate speech segment and an actual value of the first acoustic feature at a beginning of the subsequent candidate speech segment. 
     
     
       13. The system of  claim 1 , wherein the predicted statistical parameters for each of the plurality of acoustic features associated with each target unit are determined using a statistical model. 
     
     
       14. The system of  claim 13 , wherein the statistical model is composed by a mixture of probability distributions. 
     
     
       15. The system of  claim 13 , wherein the statistical model is configured to:
 receive, as inputs, the plurality of linguistic features associated with a respective target unit; and 
 output the predicted statistical parameters for each of the plurality of acoustic features associated with the respective target unit. 
 
     
     
       16. The system of  claim 15 , wherein the statistical model is further configured to output one or more density weights for each of the plurality of acoustic features associated with the respective target unit. 
     
     
       17. The system of  claim 13 , wherein the statistical model is a mixture density network comprising:
 an input layer configured to receive as inputs the plurality of linguistic features associated with a respective target unit; 
 an output layer configured to output the predicted statistical parameters for each of the plurality of acoustic features associated with the respective target unit; and 
 at least one hidden layer between the input layer and the output layer. 
 
     
     
       18. The system of  claim 13 , wherein the statistical model is configured to determine, for each target unit, the predicted statistical parameters of the second acoustic feature independent of the predicted statistical parameters of the first acoustic feature. 
     
     
       19. A method for unit-selection text-to-speech synthesis, comprising:
 at an electronic device having a processor and memory:
 receiving text to be converted to speech; 
 generating a sequence of target units representing a spoken pronunciation of the text; 
 determining, based on a plurality of linguistic features associated with each target unit of the sequence of target units, predicted statistical parameters for each of a plurality of acoustic features associated with each target unit, wherein a second acoustic feature of the plurality of acoustic features represents a change of a first acoustic feature of the plurality of acoustic features across a portion of a respective target unit of the sequence of target units; 
 selecting, based on the plurality of linguistic features associated with each target unit, a plurality of candidate speech segments corresponding to the sequence of target units; 
 for each candidate speech segment of the plurality of candidate speech segments:
 determining a target cost based on the predicted statistical parameters of the first acoustic feature associated with the respective target unit of the sequence of target units; and 
 determining a plurality of concatenation costs with respect to a plurality of subsequent candidate speech segments, the plurality of concatenation costs determined based on the predicted statistical parameters of the second acoustic feature associated with the respective target unit of the sequence of target units; 
 
 selecting from the plurality of candidate speech segments a subset of candidate speech segments for speech synthesis, the selecting based on a combined cost associated with the subset of candidate speech segments, wherein the combined cost is determined based on the target cost and the plurality of concatenation costs of each candidate speech segment; and 
 generating speech corresponding to the received text using the subset of candidate speech segments. 
 
 
     
     
       20. The method of  claim 19 , wherein the target cost for a respective candidate speech segment is based on a weighted difference between an actual value of the first acoustic feature for the respective candidate speech segment and a first predicted statistical parameter of the predicted statistical parameters of the first acoustic feature for the respective target unit, and wherein the weighted difference is weighted by a second predicted statistical parameter of the predicted statistical parameters of the first acoustic feature for the respective target unit. 
     
     
       21. The method of  claim 19 , wherein a concatenation cost of the plurality of concatenation costs for a respective candidate speech segment includes a second weighted difference between an actual value of the second acoustic feature for the respective candidate speech segment with respect to a subsequent candidate speech segment of the plurality of subsequent candidate speech segments and a first predicted statistical parameter of the predicted statistical parameters of the second acoustic feature for the respective target unit, and wherein the second weighted difference is weighted by a second predicted statistical parameter of the predicted statistical parameters of the second acoustic feature for the respective target unit. 
     
     
       22. The method of  claim 21 , wherein the actual value of the second acoustic feature for the respective candidate speech segment with respect to the subsequent candidate speech segment of the plurality of subsequent candidate speech segments comprises a difference between an actual value of the first acoustic feature at an end of the respective candidate speech segment and an actual value of the first acoustic feature at a beginning of the subsequent candidate speech segment. 
     
     
       23. The method of  claim 19 , wherein the portion of the respective target unit is an end portion of the respective target unit. 
     
     
       24. A non-transitory computer-readable storage medium comprising computer-readable instructions which, when executed by one or more processors, cause the one or more processors to:
 receive text to be converted to speech; 
 generate a sequence of target units representing a spoken pronunciation of the text; 
 determine, based on a plurality of linguistic features associated with each target unit of the sequence of target units, predicted statistical parameters for each of a plurality of acoustic features associated with each target unit, wherein a second acoustic feature of the plurality of acoustic features represents a change of a first acoustic feature of the plurality of acoustic features across a portion of a respective target unit of the sequence of target units; 
 select, based on the plurality of linguistic features associated with each target unit, a plurality of candidate speech segments corresponding to the sequence of target units; 
 for each candidate speech segment of the plurality of candidate speech segments:
 determine a target cost based on the predicted statistical parameters of the first acoustic feature associated with the respective target unit of the sequence of target units; and 
 determine a plurality of concatenation costs with respect to a plurality of subsequent candidate speech segments, the plurality of concatenation costs determined based on the predicted statistical parameters of the second acoustic feature associated with the respective target unit of the sequence of target units; 
 
 select from the plurality of candidate speech segments a subset of candidate speech segments for speech synthesis, the selecting based on a combined cost associated with the subset of candidate speech segments, wherein the combined cost is determined based on the target cost and the plurality of concatenation costs of each candidate speech segment; and 
 generate speech corresponding to the received text using the subset of candidate speech segments. 
 
     
     
       25. The computer-readable storage medium of  claim 24 , wherein the portion of the respective target unit is an end portion of the respective target unit. 
     
     
       26. The computer-readable storage medium of  claim 24 , wherein the target cost for a respective candidate speech segment is based on a weighted difference between an actual value of the first acoustic feature for the respective candidate speech segment and a first predicted statistical parameter of the predicted statistical parameters of the first acoustic feature for the respective target unit, and wherein the weighted difference is weighted by a second predicted statistical parameter of the predicted statistical parameters of the first acoustic feature for the respective target unit. 
     
     
       27. The computer-readable storage medium of  claim 24 , wherein a concatenation cost of the plurality of concatenation costs for a respective candidate speech segment includes a second weighted difference between an actual value of the second acoustic feature for the respective candidate speech segment with respect to a subsequent candidate speech segment of the plurality of subsequent candidate speech segments and a first predicted statistical parameter of the predicted statistical parameters of the second acoustic feature for the respective target unit, and wherein the second weighted difference is weighted by a second predicted statistical parameter of the predicted statistical parameters of the second acoustic feature for the respective target unit. 
     
     
       28. The computer-readable storage medium of  claim 27 , wherein the actual value of the second acoustic feature for the respective candidate speech segment with respect to the subsequent candidate speech segment of the plurality of subsequent candidate speech segments comprises a difference between an actual value of the first acoustic feature at an end of the respective candidate speech segment and an actual value of the first acoustic feature at a beginning of the subsequent candidate speech segment.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Ser. No. 62/341,948, filed on May 26, 2016, entitled UNIT-SELECTION TEXT-TO-SPEECH SYNTHESIS BASED ON PREDICTED CONCATENATION PARAMETERS, which is hereby incorporated by reference in its entirety for all purposes. 
    
    
     FIELD 
     The present disclosure relates generally to text-to-speech synthesis, and more specifically to techniques for performing unit-selection text-to-speech synthesis. 
     BACKGROUND 
     Unit-selection text-to-speech (TTS) synthesis can be desirable for producing a more natural-sounding voice quality compared to other TTS methods. Conventionally, unit-selection TTS synthesis can include three stages: front-end text analysis, unit selection, and waveform synthesis. In the unit-selection stage, a unit-selection algorithm can be implemented to select a sequence of speech units (e.g., speech segments, phones, sub-phones, etc.) from a database of audio units. The speech units can be obtained by segmenting recordings of a voice talent&#39;s speech that represent the spoken form of a corpus of text. Implementing a sophisticated unit-selection algorithm can be desirable to select the most suitable speech units from the database. The most suitable audio units can have acoustic properties that best match the target pronunciation of the text to be converted to speech, which can enable the synthesis of high-quality, natural sounding speech. 
     BRIEF SUMMARY 
     Systems and processes for performing unit-selection text-to-speech synthesis are provided. In one example process, text to be converted to speech is received. A sequence of target units representing a spoken pronunciation of the text is generated. Predicted statistical parameters for each of a plurality of acoustic features associated with each target unit of the sequence of target units are determined based on a plurality of linguistic features associated with each target unit. A plurality of candidate speech segments corresponding to the sequence of target units are selected based on the plurality of linguistic features associated with each target unit. A target cost for each candidate speech segment of the plurality of candidate speech segments is determined based on the predicted statistical parameters of a first acoustic feature of the plurality of acoustic features associated with a respective target unit of the sequence of target units. A plurality of concatenation costs with respect to a plurality of subsequent candidate speech segments are determined for each candidate speech segment of the plurality of candidate speech segments. The plurality of concatenation costs are determined based on the predicted statistical parameters of a second acoustic feature of the plurality of acoustic features associated with the respective target unit of the sequence of target units. A subset of candidate speech segments is selected from the plurality of candidate speech segments for speech synthesis. The subset of candidate speech segments is selected based on a combined cost associated with the subset of candidate speech segments. The combined cost is determined based on the target cost and the plurality of concatenation costs of each candidate speech segment. Speech corresponding to the received text is generated using the subset of candidate speech segments. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       For a better understanding of the various described embodiments, reference should be made to the Description of Embodiments below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures. 
         FIG. 1A  is a block diagram illustrating a portable multifunction device with a touch-sensitive display in accordance with some examples. 
         FIG. 1B  is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. 
         FIG. 2  illustrates a portable multifunction device having a touch screen in accordance with some embodiments. 
         FIG. 3  is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. 
         FIGS. 4A and 4B  illustrate an exemplary user interface for a menu of applications on a portable multifunction device in accordance with some embodiments. 
         FIG. 5  illustrates an exemplary schematic block diagram of a text-to-speech module in accordance with some embodiments. 
         FIG. 6  illustrates an exemplary block diagram of a speech segment generation module in accordance with some embodiments. 
         FIG. 7  illustrates a flow diagram of an exemplary process for unit-selection text-to-speech synthesis in accordance with some embodiments. 
         FIG. 8  illustrates an exemplary sequence of target units with one or more candidate speech segments selected for each target unit in accordance with some embodiments. 
         FIG. 9  illustrates an exemplary mixture density network for determining predicted statistical parameters for acoustic features associated with a respective target unit in accordance with some embodiments. 
         FIG. 10  illustrates a flow diagram of an exemplary process for generating a database of speech segments used for unit-selection text-to-speech synthesis in accordance with some embodiments. 
         FIG. 11  illustrates a functional block diagram of an electronic device in accordance with some embodiments. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     In the following description of the disclosure and embodiments, reference is made to the accompanying drawings in which it is shown by way of illustration of specific embodiments that can be practiced. It is to be understood that other embodiments and examples can be practiced and changes can be made without departing from the scope of the disclosure. 
     In some conventional unit-selection text-to-speech synthesis processes, target costs are calculated for candidate speech segments to determine how well the actual acoustic features of the candidate speech segments match with the predicted acoustic features of the corresponding target units. Additionally, concatenation costs are calculated for every pair of consecutive candidate speech segments to determine how well each pair concatenates. For example, the concatenation costs indicate the differences in acoustic features between pairs of consecutive candidate speech segments. The candidate speech segments that result in the lowest combined cost based on the calculated target costs and concatenation costs are then selected for speech synthesis. Thus, in these conventional processes, pairs of consecutive candidate speech segments having the lowest concatenation costs tend to be selected for speech synthesis. However, in natural speech, there can be inherent differences in the acoustic features between pairs of consecutive speech segments. For example, the pitch between a pair of consecutive speech segments can be rising or falling at a particular rate, which results in an inherent difference in pitch between the speech segments. Minimizing these differences by selecting consecutive pairs of candidate speech segments having the lowest concatenation costs for speech synthesis may thus result in less natural sounding speech. In accordance with exemplary systems and processes described herein, it may be desirable to compare the actual differences in acoustic features between consecutive pairs of candidate speech segments with the predicted differences in acoustic features associated with the corresponding target units. 
     In one example process for unit-selection text-to-speech synthesis, text to be converted to speech is received. A sequence of target units representing a spoken pronunciation of the text is generated. Predicted statistical parameters for each of a plurality of acoustic features associated with each target unit of the sequence of target units are determined based on a plurality of linguistic features associated with each target unit. A plurality of candidate speech segments corresponding to the sequence of target units are selected based on the plurality of linguistic features associated with each target unit. A target cost for each candidate speech segment of the plurality of candidate speech segments is determined based on the predicted statistical parameters of a first acoustic feature of the plurality of acoustic features associated with a respective target unit of the sequence of target units. A plurality of concatenation costs with respect to a plurality of subsequent candidate speech segments are determined for each candidate speech segment of the plurality of candidate speech segments. The plurality of concatenation costs are determined based on the predicted statistical parameters of a second acoustic feature of the plurality of acoustic features associated with the respective target unit of the sequence of target units. In some examples, the predicted statistical parameters of the second acoustic feature represent the predicted difference of the first acoustic feature between the respective target unit and the subsequent target unit. In these examples, the concatenation cost represents a comparison of the actual differences in acoustic features between consecutive pairs of candidate speech segments with the predicted differences in acoustic features between corresponding target units. A subset of candidate speech segments is selected from the plurality of candidate speech segments for speech synthesis. The subset of candidate speech segments is selected based on a combined cost associated with the subset of candidate speech segments. The combined cost is determined based on the target cost and the plurality of concatenation costs of each candidate speech segment. Speech corresponding to the received text is generated using the subset of candidate speech segments. 
     Although the following description uses terms first, second, etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first candidate speech segment could be termed a second candidate speech segment, and, similarly, a second candidate speech segment contact could be termed a first candidate speech segment, without departing from the scope of the present invention. The first candidate speech segment and the candidate speech segment contact are both candidate speech segment, but they are not the same candidate speech segment. 
     The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a”, “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context. 
     Embodiments of electronic devices, systems for providing embedded phrases on such devices, and associated processes for using such devices are described. In some embodiments, the device is a portable communications device, such as a mobile telephone, that also contains other functions, such as PDA and/or music player functions. Exemplary embodiments of portable multifunction devices include, without limitation, the iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other portable devices, such as laptops or tablet computers with touch-sensitive surfaces (e.g., touch screen displays and/or touch pads), may also be used. Exemplary embodiments of laptop and tablet computers include, without limitation, the iPad® and MacBook® devices from Apple Inc. of Cupertino, Calif. It should also be understood that, in some embodiments, the device is not a portable communications device, but is a desktop computer. Exemplary embodiments of desktop computers include, without limitation, the Mac Pro® from Apple Inc. of Cupertino, Calif. 
     In the discussion that follows, an electronic device that includes a display and a touch-sensitive surface is described. It should be understood, however, that the electronic device optionally includes one or more other physical user-interface devices, such as button(s), a physical keyboard, a mouse, and/or a joystick. 
     The device may support a variety of applications, such as one or more of the following: a drawing application, a presentation application, a word processing application, a website creation application, a disk authoring application, a spreadsheet application, a gaming application, a telephone application, a video conferencing application, an e-mail application, an instant messaging application, a workout support application, a photo management application, a digital camera application, a digital video camera application, a web browsing application, a digital music player application, and/or a digital video player application. 
     The various applications that are executed on the device optionally use at least one common physical user-interface device, such as the touch-sensitive surface. One or more functions of the touch-sensitive surface as well as corresponding information displayed on the device are, optionally, adjusted and/or varied from one application to the next and/or within a respective application. In this way, a common physical architecture (such as the touch-sensitive surface) of the device optionally supports the variety of applications with user interfaces that are intuitive and transparent to the user. 
       FIGS. 1A and 1B  are block diagrams illustrating exemplary portable multifunction device  100  with touch-sensitive displays  112  in accordance with some embodiments. Touch-sensitive display  112  is sometimes called a “touch screen” for convenience. Device  100  includes memory  102 . Device  100  includes memory controller  122 , one or more processing units (CPU&#39;s)  120 , peripherals interface  118 , RF circuitry  108 , audio circuitry  110 , speaker  111 , microphone  113 , input/output (I/O) subsystem  106 , other input or control devices  116 , and external port  124 . Device  100  includes one or more optical sensors  164 . Bus/signal lines  103  allows these components to communicate with one another. Device  100  is one example of an electronic device that could be used to perform the techniques described herein. Specific implementations involving device  100  may have more or fewer components than shown, may combine two or more components, or may have a different configuration or arrangement of the components. The various components shown in  FIGS. 1A and 1B  may be implemented in hardware, software, or a combination of both. The components also can be implemented using one or more signal processing and/or application specific integrated circuits. 
     Memory  102  includes one or more computer readable storage mediums. The computer readable storage mediums may be tangible and non-transitory. The computer-readable storage mediums are optionally transitory. Memory  102  may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller  122  may control access to memory  102  by other components of device  100 . 
     Peripherals interface  118  is used to couple input and output peripherals of the device to CPU  120  and memory  102 . The one or more processors  120  run or execute various software programs and/or sets of instructions stored in memory  102  to perform various functions for device  100  and to process data. In some embodiments, peripherals interface  118 , CPU  120 , and memory controller  122  is implemented on a single chip, such as chip  104 . In some other embodiments, they may be implemented on separate chips. 
     RF (radio frequency) circuitry  108  receives and sends RF signals, also called electromagnetic signals. RF circuitry  108  converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry  108  includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry  108  communicates with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The wireless communication may use any of a plurality of communications standards, protocols and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 502.11a, IEEE 502.11b, IEEE 802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e-mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document. 
     Audio circuitry  110 , speaker  111 , and microphone  113  provide an audio interface between a user and device  100 . Audio circuitry  110  receives audio data from peripherals interface  118 , converts the audio data to an electrical signal, and transmits the electrical signal to speaker  111 . Speaker  111  converts the electrical signal to human-audible sound waves. Audio circuitry  110  also receives electrical signals converted by microphone  113  from sound waves. Audio circuitry  110  converts the electrical signal to audio data and transmits the audio data to peripherals interface  118  for processing. Audio data may be retrieved from and/or transmitted to memory  102  and/or RF circuitry  108  by peripherals interface  118 . In some embodiments, audio circuitry  110  also includes a headset jack (e.g.,  212 ,  FIG. 2 ). The headset jack provides an interface between audio circuitry  110  and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone). 
     I/O subsystem  106  couples input/output peripherals on device  100 , such as touch screen  112  and other input control devices  116 , to peripherals interface  118 . I/O subsystem  106  includes display controller  156  and one or more input controllers  160  for other input or control devices. The one or more input controllers  160  receive/send electrical signals from/to other input or control devices  116 . The other input control devices  116  include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s)  160  is coupled to any (or none) of the following: a keyboard, infrared port, USB port, and a pointer device such as a mouse. The one or more buttons (e.g.,  208 ,  FIG. 2 ) include an up/down button for volume control of speaker  111  and/or microphone  113 . The one or more buttons may include a push button (e.g.,  206 ,  FIG. 2 ). A quick press of the push button disengages a lock of touch screen  112  or begin a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g.,  206 ) turns power to device  100  on or off. The user may be able to customize a functionality of one or more of the buttons. Touch screen  112  is used to implement virtual or soft buttons and one or more soft keyboards. 
     Touch-sensitive display  112  provides an input interface and an output interface between the device and a user. Display controller  156  receives and/or sends electrical signals from/to touch screen  112 . Touch screen  112  displays visual output to the user. The visual output may include graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output may correspond to user-interface objects. 
     Touch screen  112  has a touch-sensitive surface, sensor or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen  112  and display controller  156  (along with any associated modules and/or sets of instructions in memory  102 ) detect contact (and any movement or breaking of the contact) on touch screen  112  and converts the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web-pages or images) that are displayed on touch screen  112 . In an exemplary embodiment, a point of contact between touch screen  112  and the user corresponds to a finger of the user. 
     In some examples, touch screen  112  uses LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies may be used in other embodiments. Touch screen  112  and display controller  156  detects contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen  112 . In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif. 
     A touch-sensitive display in some embodiments of touch screen  112  may be analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen  112  displays visual output from device  100 , whereas touch sensitive touchpads do not provide visual output. 
     A touch-sensitive display in some embodiments of touch screen  112  may be as described in the following applications: (1) U.S. patent application Ser. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. patent application Ser. No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30, 2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patent application Ser. No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patent application Ser. No. 11/228,758, “Virtual Input Device Placement On A Touch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patent application Ser. No. 11/228,700, “Operation Of A Computer With A Touch Screen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser. No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No. 11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. All of these applications are incorporated by reference herein in their entirety. 
     In some examples, touch screen  112  has a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user can make contact with touch screen  112  using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen. In some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user. 
     In some embodiments, in addition to the touch screen, device  100  includes a touchpad (not shown) for activating or deactivating particular functions. In some embodiments, the touchpad is a touch-sensitive area of the device that, unlike the touch screen, does not display visual output. The touchpad is a touch-sensitive surface that is separate from touch screen  112  or an extension of the touch-sensitive surface formed by the touch screen. 
     Device  100  also includes power system  162  for powering the various components. Power system  162  includes a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices. 
     Device  100  also includes one or more optical sensors  164 .  FIGS. 1A and 1B  show an optical sensor coupled to optical sensor controller  158  in I/O subsystem  106 . Optical sensor  164  includes charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor  164  receives light from the environment, projected through one or more lens, and converts the light to data representing an image. In conjunction with imaging module  143  (also called a camera module), optical sensor  164  captures still images or video. In some embodiments, an optical sensor is located on the back of device  100 , opposite touch screen display  112  on the front of the device, so that the touch screen display may be used as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user&#39;s image may be obtained for videoconferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor  164  can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor  164  may be used along with the touch screen display for both video conferencing and still and/or video image acquisition. 
     In some examples, device  100  also includes one or more proximity sensors  166 .  FIGS. 1A and 1B  show proximity sensor  166  coupled to peripherals interface  118 . Alternately, proximity sensor  166  is coupled to input controller  160  in I/O subsystem  106 . Proximity sensor  166  may perform as described in U.S. patent application Ser. No. 11/241,839, “Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “Proximity Detector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and Ser. No. 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen  112  when the multifunction device is placed near the user&#39;s ear (e.g., when the user is making a phone call). 
     Device  100  optionally also includes one or more tactile output generators  167 .  FIG. 1A  shows a tactile output generator coupled to haptic feedback controller  161  in I/O subsystem  106 . Tactile output generator  167  optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor  165  receives tactile feedback generation instructions from haptic feedback module  133  and generates tactile outputs on device  100  that are capable of being sensed by a user of device  100 . In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system  112 ) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device  100 ) or laterally (e.g., back and forth in the same plane as a surface of device  100 ). In some embodiments, at least one tactile output generator sensor is located on the back of device  100 , opposite touch screen display  112 , which is located on the front of device  100 . 
     Device  100  also includes one or more accelerometers  168 .  FIGS. 1A and 1B  show accelerometer  168  coupled to peripherals interface  118 . Alternately, accelerometer  168  is coupled to an input controller  160  in I/O subsystem  106 . Accelerometer  168  may perform as described in U.S. Patent Publication No. 20050190059, “Acceleration-based Theft Detection System for Portable Electronic Devices,” and U.S. Patent Publication No. 20060017692, “Methods And Apparatuses For Operating A Portable Device Based On An Accelerometer,” both of which are incorporated by reference herein in their entirety. In some embodiments, information is displayed on the touch screen display in a portrait view or a landscape view based on an analysis of data received from the one or more accelerometers. Device  100  optionally includes, in addition to accelerometer(s)  168 , a magnetometer (not shown) and a GPS (or GLONASS or other global navigation system) receiver (not shown) for obtaining information concerning the location and orientation (e.g., portrait or landscape) of device  100 . 
     In some embodiments, the software components stored in memory  102  include operating system  126 , communication module (or set of instructions)  128 , contact/motion module (or set of instructions)  130 , graphics module (or set of instructions)  132 , text input module (or set of instructions)  134 , Global Positioning System (GPS) module (or set of instructions)  135 , and applications (or sets of instructions)  136 . Furthermore, in some embodiments memory  102  stores device/global internal state  157 , as shown in  FIGS. 1A, 1B and 3 . Device/global internal state  157  includes one or more of: active application state, indicating which applications, if any, are currently active; display state, indicating what applications, views or other information occupy various regions of touch screen display  112 ; sensor state, including information obtained from the device&#39;s various sensors and input control devices  116 ; and location information concerning the device&#39;s location and/or attitude. 
     Operating system  126  (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components. 
     Communication module  128  facilitates communication with other devices over one or more external ports  124  and also includes various software components for handling data received by RF circuitry  108  and/or external port  124 . External port  124  (e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin connector that is the same as, or similar to and/or compatible with the 5-pin and/or 30-pin connectors used on devices made by Apple Inc. 
     Contact/motion module  130  detects contact with touch screen  112  (in conjunction with display controller  156 ) and other touch sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module  130  includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module  130  receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, may include determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations may be applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module  130  and display controller  156  detects contact on a touchpad. In some embodiments, contact/motion module  130  and controller  160  detects contact on a click wheel. 
     Contact/motion module  130  detects a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns. Thus, a gesture is detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes detecting a finger-down event followed by detecting a finger-up (lift off) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger-down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (lift off) event. 
     Graphics module  132  includes various known software components for rendering and displaying graphics on touch screen  112  or other display, including components for changing the intensity of graphics that are displayed. As used herein, the term “graphics” includes any object that can be displayed to a user, including without limitation text, web-pages, icons (such as user-interface objects including soft keys), digital images, videos, animations and the like. In some embodiments, graphics module  132  stores data representing graphics to be used. Each graphic may be assigned a corresponding code. Graphics module  132  receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller  156 . 
     Haptic feedback module  133  includes various software components for generating instructions used by tactile output generator(s)  167  to produce tactile outputs at one or more locations on device  100  in response to user interactions with device  100 . 
     Text input module  134 , which may be a component of graphics module  132 , provides soft keyboards for entering text in various applications (e.g., contacts  137 , e-mail  140 , IM  141 , browser  147 , and any other application that needs text input). 
     GPS module  135  determines the location of the device and provides this information for use in various applications (e.g., to telephone  138  for use in location-based dialing, to camera  143  as picture/video metadata, and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets). 
     Applications  136  include the following modules (or sets of instructions), or a subset or superset thereof:
         Contacts module  137  (sometimes called an address book or contact list);   Telephone module  138 ;   Video conferencing module  139 ;   E-mail client module  140 ;   Instant messaging (IM) module  141 ;   Workout support module  142 ;   Camera module  143  for still and/or video images;   Image management module  144 ;   Video player module;   Music player module;   Browser module  147 ;   Calendar module  148 ;   Widget modules  149 , which include one or more of: weather widget  149 - 1 , stocks widget  149 - 2 , calculator widget  149 - 3 , alarm clock widget  149 - 4 , dictionary widget  149 - 5 , and other widgets obtained by the user, as well as user-created widgets  149 - 6 ;   Widget creator module  150  for making user-created widgets  149 - 6 ;   Search module  151 ;   Video and music player module  152 , which merges video player module and music player module;   Notes module  153 ;   Map module  154 ; and/or   Online video module  155 .       

     Examples of other applications  136  that may be stored in memory  102  include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , contacts module  137  is used to manage an address book or contact list (e.g., stored in application internal state  192  of contacts module  137  in memory  102  or memory  370 ), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone  138 , video conference module  139 , e-mail  140 , or IM  141 ; and so forth. 
     In conjunction with RF circuitry  108 , audio circuitry  110 , speaker  111 , microphone  113 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , telephone module  138  is used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in address book  137 , modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation and disconnect or hang up when the conversation is completed. As noted above, the wireless communication may use any of a plurality of communications standards, protocols and technologies. 
     In conjunction with RF circuitry  108 , audio circuitry  110 , speaker  111 , microphone  113 , touch screen  112 , display controller  156 , optical sensor  164 , optical sensor controller  158 , contact module  130 , graphics module  132 , text input module  134 , contacts module  137 , and telephone module  138 , video conference module  139  includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , e-mail client module  140  includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module  144 , e-mail client module  140  makes it very easy to create and send e-mails with still or video images taken with camera module  143 . 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact module  130 , graphics module  132 , and text input module  134 , the instant messaging module  141  includes executable instructions to enter a sequence of characters corresponding to an instant message, to modify previously entered characters, to transmit a respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages and to view received instant messages. In some embodiments, transmitted and/or received instant messages may include graphics, photos, audio files, video files and/or other attachments as are supported in a MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS). 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact module  130 , graphics module  132 , text input module  134 , GPS module  135 , map module  154 , and music player module, workout support module  142  includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store and transmit workout data. 
     In conjunction with touch screen  112 , display controller  156 , optical sensor(s)  164 , optical sensor controller  158 , contact/motion module  130 , graphics module  132 , and image management module  144 , camera module  143  includes executable instructions to capture still images or video (including a video stream) and store them into memory  102 , modify characteristics of a still image or video, or delete a still image or video from memory  102 . 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , and camera module  143 , image management module  144  includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , audio circuitry  110 , and speaker  111 , video player module  145  includes executable instructions to display, present or otherwise play back videos (e.g., on touch screen  112  or on an external, connected display via external port  124 ). 
     In conjunction with touch screen  112 , display system controller  156 , contact module  130 , graphics module  132 , audio circuitry  110 , speaker  111 , RF circuitry  108 , and browser module  147 , music player module  146  includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files. In some embodiments, device  100  includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.). 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , browser module  147  includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web-pages or portions thereof, as well as attachments and other files linked to web-pages. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , e-mail client module  140 , and browser module  147 , calendar module  148  includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to do lists, etc.) in accordance with user instructions. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , and browser module  147 , widget modules  149  are mini-applications that may be downloaded and used by a user (e.g., weather widget  149 - 1 , stocks widget  149 - 2 , calculator widget  149 - 3 , alarm clock widget  149 - 4 , and dictionary widget  149 - 5 ) or created by the user (e.g., user-created widget  149 - 6 ). In some embodiments, a widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! Widgets). 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , and browser module  147 , the widget creator module  150  is used by a user to create widgets (e.g., turning a user-specified portion of a web-page into a widget). 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , search module  151  includes executable instructions to search for text, music, sound, image, video, and/or other files in memory  102  that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , audio circuitry  110 , speaker  111 , RF circuitry  108 , and browser module  147 , video and music player module  152  includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen  112  or on an external, connected display via external port  124 ). In some embodiments, device  100  optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.). 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , notes module  153  includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , GPS module  135 , and browser module  147 , map module  154  is used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions; data on stores and other points of interest at or near a particular location; and other location-based data) in accordance with user instructions. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , audio circuitry  110 , speaker  111 , RF circuitry  108 , text input module  134 , e-mail client module  140 , and browser module  147 , online video module  155  includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port  124 ), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module  141 , rather than e-mail client module  140 , is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety. 
     Each of the above identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise rearranged in various embodiments. For example, video player module may be combined with music player module into a single module (e.g., video and music player module  152 ,  FIG. 1B ). In some embodiments, memory  102  stores a subset of the modules and data structures identified above. Furthermore, memory  102  stores additional modules and data structures not described above. 
     In some embodiments, device  100  is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device  100 , the number of physical input control devices (such as push buttons, dials, and the like) on device  100  may be reduced. 
     The predefined set of functions that may be performed exclusively through a touch screen and/or a touchpad include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device  100  to a main, home, or root menu from any user interface that may be displayed on device  100 . In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad. 
       FIG. 1B  is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory  102  (in  FIG. 1A ) or  370  ( FIG. 3 ) includes event sorter  170  (e.g., in operating system  126 ) and a respective application  136 - 1  (e.g., any of the aforementioned applications  137 - 151 ,  155 ,  380 - 390 ). 
     Event sorter  170  receives event information and determines the application  136 - 1  and application view  191  of application  136 - 1  to which to deliver the event information. Event sorter  170  includes event monitor  171  and event dispatcher module  174 . In some embodiments, application  136 - 1  includes application internal state  192 , which indicates the current application view(s) displayed on touch sensitive display  112  when the application is active or executing. In some embodiments, device/global internal state  157  is used by event sorter  170  to determine which application(s) is(are) currently active, and application internal state  192  is used by event sorter  170  to determine application views  191  to which to deliver event information. 
     In some embodiments, application internal state  192  includes additional information, such as one or more of: resume information to be used when application  136 - 1  resumes execution, user interface state information that indicates information being displayed or that is ready for display by application  136 - 1 , a state queue for enabling the user to go back to a prior state or view of application  136 - 1 , and a redo/undo queue of previous actions taken by the user. 
     Event monitor  171  receives event information from peripherals interface  118 . Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display  112 , as part of a multi-touch gesture). Peripherals interface  118  transmits information it receives from I/O subsystem  106  or a sensor, such as proximity sensor  166 , accelerometer(s)  168 , and/or microphone  113  (through audio circuitry  110 ). Information that peripherals interface  118  receives from I/O subsystem  106  includes information from touch-sensitive display  112  or a touch-sensitive surface. 
     In some embodiments, event monitor  171  sends requests to the peripherals interface  118  at predetermined intervals. In response, peripherals interface  118  transmits event information. In other embodiments, peripherals interface  118  transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration). In some embodiments, event sorter  170  also includes a hit view determination module  172  and/or an active event recognizer determination module  173 . 
     Hit view determination module  172  provides software procedures for determining where a sub-event has taken place within one or more views, when touch sensitive display  112  displays more than one view. Views are made up of controls and other elements that a user can see on the display. 
     Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected may correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected may be called the hit view, and the set of events that are recognized as proper inputs may be determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture. 
     Hit view determination module  172  receives information related to sub-events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module  172  identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module  172 , the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view. 
     Active event recognizer determination module  173  determines which view or views within a view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module  173  determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module  173  determines that all views that include the physical location of a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views. 
     Event dispatcher module  174  dispatches the event information to an event recognizer (e.g., event recognizer  180 ). In embodiments including active event recognizer determination module  173 , event dispatcher module  174  delivers the event information to an event recognizer determined by active event recognizer determination module  173 . In some embodiments, event dispatcher module  174  stores in an event queue the event information, which is retrieved by a respective event receiver  182 . 
     In some embodiments, operating system  126  includes event sorter  170 . Alternatively, application  136 - 1  includes event sorter  170 . In yet other embodiments, event sorter  170  is a stand-alone module, or a part of another module stored in memory  102 , such as contact/motion module  130 . 
     In some embodiments, application  136 - 1  includes a plurality of event handlers  190  and one or more application views  191 , each of which includes instructions for handling touch events that occur within a respective view of the application&#39;s user interface. Each application view  191  of the application  136 - 1  includes one or more event recognizers  180 . Typically, a respective application view  191  includes a plurality of event recognizers  180 . In other embodiments, one or more of event recognizers  180  are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application  136 - 1  inherits methods and other properties. In some embodiments, a respective event handler  190  includes one or more of: data updater  176 , object updater  177 , GUI updater  178 , and/or event data  179  received from event sorter  170 . Event handler  190  utilizes or calls data updater  176 , object updater  177 , or GUI updater  178  to update the application internal state  192 . Alternatively, one or more of the application views  191  include one or more respective event handlers  190 . Also, in some embodiments, one or more of data updater  176 , object updater  177 , and GUI updater  178  are included in a respective application view  191 . 
     A respective event recognizer  180  receives event information (e.g., event data  179 ) from event sorter  170  and identifies an event from the event information. Event recognizer  180  includes event receiver  182  and event comparator  184 . In some embodiments, event recognizer  180  also includes at least a subset of: metadata  183 , and event delivery instructions  188  (which may include sub-event delivery instructions). 
     Event receiver  182  receives event information from event sorter  170 . The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch the event information may also include speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device attitude) of the device. 
     Event comparator  184  compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub-event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator  184  includes event definitions  186 . Event definitions  186  contain definitions of events (e.g., predefined sequences of sub-events), for example, event  1  ( 187 - 1 ), event  2  ( 187 - 2 ), and others. In some embodiments, sub-events in an event ( 187 ) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event  1  ( 187 - 1 ) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event  2  ( 187 - 2 ) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display  112 , and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers  190 . 
     In some embodiments, event definitions  187  include a definition of an event for a respective user-interface object. In some embodiments, event comparator  184  performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display  112 , when a touch is detected on touch-sensitive display  112 , event comparator  184  performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective event handler  190 , the event comparator uses the result of the hit test to determine which event handler  190  should be activated. For example, event comparator  184  selects an event handler associated with the sub-event and the object triggering the hit test. 
     In some embodiments, the definition for a respective event ( 187 ) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer&#39;s event type. 
     When a respective event recognizer  180  determines that the series of sub-events do not match any of the events in event definitions  186 , the respective event recognizer  180  enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture. 
     In some embodiments, a respective event recognizer  180  includes metadata  183  with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata  183  includes configurable properties, flags, and/or lists that indicate how event recognizers may interact, or are enabled to interact, with one another. In some embodiments, metadata  183  includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy. 
     In some embodiments, a respective event recognizer  180  activates event handler  190  associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer  180  delivers event information associated with the event to event handler  190 . Activating an event handler  190  is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer  180  throws a flag associated with the recognized event, and event handler  190  associated with the flag catches the flag and performs a predefined process. 
     In some embodiments, event delivery instructions  188  include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated with the series of sub-events or with actively involved views receive the event information and perform a predetermined process. 
     In some embodiments, data updater  176  creates and updates data used in application  136 - 1 . For example, data updater  176  updates the telephone number used in contacts module  137 , or stores a video file used in video player module. In some embodiments, object updater  177  creates and updates objects used in application  136 - 1 . For example, object updater  177  creates a new user-interface object or updates the position of a user-interface object. GUI updater  178  updates the GUI. For example, GUI updater  178  prepares display information and sends it to graphics module  132  for display on a touch-sensitive display. 
     In some embodiments, event handler(s)  190  includes or has access to data updater  176 , object updater  177 , and GUI updater  178 . In some embodiments, data updater  176 , object updater  177 , and GUI updater  178  are included in a single module of a respective application  136 - 1  or application view  191 . In other embodiments, they are included in two or more software modules. 
     It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices  100  with input devices, not all of which are initiated on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs; movement of the device; oral instructions; detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub-events which define an event to be recognized. 
       FIG. 2  illustrates a portable multifunction device  100  having a touch screen  112  in accordance with some embodiments. The touch screen displays one or more graphics within user interface (UI)  200 . In this embodiment, as well as others described below, a user selects one or more of the graphics by making contact or touching the graphics, for example, with one or more fingers  202  (not drawn to scale in the figure) or one or more styluses  203  (not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the contact may include a gesture, such as one or more taps, one or more swipes (from left to right, right to left, upward and/or downward) and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device  100 . In some embodiments, inadvertent contact with a graphic may not select the graphic. For example, a swipe gesture that sweeps over an application icon may not select the corresponding application when the gesture corresponding to selection is a tap. 
     Device  100  also includes one or more physical buttons, such as “home” or menu button  204 . As described previously, menu button  204  is used to navigate to any application  136  in a set of applications that may be executed on device  100 . Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen  112 . 
     In one embodiment, device  100  includes touch screen  112 , menu button  204 , push button  206  for powering the device on/off and locking the device, volume adjustment button(s)  208 , Subscriber Identity Module (SIM) card slot  210 , head set jack  212 , and docking/charging external port  124 . Push button  206  is used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device  100  also may accept verbal input for activation or deactivation of some functions through microphone  113 . 
       FIG. 3  is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device  300  need not be portable. In some embodiments, device  300  is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child&#39;s learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device  300  typically includes one or more processing units (CPU&#39;s)  310 , one or more network or other communications interfaces  360 , memory  370 , and one or more communication buses  320  for interconnecting these components. Communication buses  320  includes circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device  300  includes input/output (I/O) interface  330  comprising display  340 , which is typically a touch screen display. I/O interface  330  also includes a keyboard and/or mouse (or other pointing device)  350  and touchpad  355 . Memory  370  includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices; and includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory  370  optionally includes one or more storage devices remotely located from CPU(s)  310 . In some embodiments, memory  370  stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory  102  of portable multifunction device  100  ( FIG. 1 ), or a subset thereof. Furthermore, memory  370  stores additional programs, modules, and data structures not present in memory  102  of portable multifunction device  100 . For example, memory  370  of device  300  stores drawing module  380 , presentation module  382 , word processing module  384 , website creation module  386 , disk authoring module  388 , and/or spreadsheet module  390 , while memory  102  of portable multifunction device  100  ( FIG. 1 ) may not store these modules. 
     Each of the above identified elements in  FIG. 3  can be stored in one or more of the previously mentioned memory devices. Each of the above identified modules corresponds to a set of instructions for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory  370  stores a subset of the modules and data structures identified above. Furthermore, memory  370  stores additional modules and data structures not described above. 
     Attention is now directed towards embodiments of user interfaces (“UI”) that may be implemented on portable multifunction device  100 .  FIG. 4A  illustrates exemplary user interfaces for a menu of applications on portable multifunction device  100  in accordance with some embodiments. Similar user interfaces may be implemented on device  300 . In some embodiments, user interface  400  includes the following elements, or a subset or superset thereof:
         Signal strength indicator(s)  402  for wireless communication(s), such as cellular and Wi-Fi signals;   Time  404 ;   Bluetooth indicator  405 ;   Battery status indicator  406 ;   Tray  408  with icons for frequently used applications, such as:
           Icon  416  for telephone module  138 , labeled “Phone,” which optionally includes an indicator  414  of the number of missed calls or voicemail messages;   Icon  418  for e-mail client module  140 , labeled “Mail,” which optionally includes an indicator  410  of the number of unread e-mails;   Icon  420  for browser module  147 , labeled “Browser;” and   Icon  422  for video and music player module  152 , also referred to as iPod (trademark of Apple Inc.) module  152 , labeled “iPod;” and   
           Icons for other applications, such as:
           Icon  424  for IM module  141 , labeled “Messages;”   Icon  426  for calendar module  148 , labeled “Calendar;”   Icon  428  for image management module  144 , labeled “Photos;”   Icon  430  for camera module  143 , labeled “Camera;”   Icon  432  for online video module  155 , labeled “Online Video;”   Icon  434  for stocks widget  149 - 2 , labeled “Stocks;”   Icon  436  for map module  154 , labeled “Maps;”   Icon  438  for weather widget  149 - 1 , labeled “Weather;”   Icon  440  for alarm clock widget  149 - 4 , labeled “Clock;”   Icon  442  for workout support module  142 , labeled “Workout Support;”   Icon  444  for notes module  153 , labeled “Notes;” and   Icon  446  for a settings application or module, labeled “Settings,” which provides access to settings for device  100  and its various applications  136 .   
               

       FIG. 4B  illustrates an exemplary user interface on a device (e.g., device  300 ,  FIG. 3 ) with a touch-sensitive surface  451  (e.g., a tablet or touchpad  355 ,  FIG. 3 ) that is separate from the display  450  (e.g., touch screen display  112 ). Although many of the examples which follow will be given with reference to inputs on touch screen display  112  (where the touch sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in  FIG. 4B . In some embodiments the touch sensitive surface (e.g.,  451 ) has a primary axis (e.g.,  452 ) that corresponds to a primary axis (e.g.,  453 ) on the display (e.g.,  450 ). In accordance with these embodiments, the device detects contacts (e.g.,  460  and  462 ) with the touch-sensitive surface  451  at locations that correspond to respective locations on the display (e.g.,  460  corresponds to  468  and  462  corresponds to  470 ). In this way, user inputs (e.g., contacts  460  and  462 , and movements thereof) detected by the device on the touch-sensitive surface (e.g.,  451 ) are used by the device to manipulate the user interface on the display (e.g.,  450 ) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods may be used for other user interfaces described herein. 
     Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a contact) followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is, optionally, replaced with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse and finger contacts are, optionally, used simultaneously. 
     As used in the specification and claims, the term “open application” refers to a software application with retained state information (e.g., as part of device/global internal state  157  and/or application internal state  192 ). An open (e.g., executing) application is any one of the following types of applications:
         an active application, which is currently displayed on display  112  (or a corresponding application view is currently displayed on the display);   a background application (or background process), which is not currently displayed on display  112 , but one or more application processes (e.g., instructions) for the corresponding application are being processed by one or more processors  120  (i.e., running);   a suspended application, which is not currently running, and the application is stored in a volatile memory (e.g., DRAM, SRAM, DDR RAM, or other volatile random access solid state memory device of memory  102 ); and   a hibernated application, which is not running, and the application is stored in a non-volatile memory (e.g., one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices of memory  102 ).       

     As used herein, the term “closed application” refers to software applications without retained state information (e.g., state information for closed applications is not stored in a memory of the device). Accordingly, closing an application includes stopping and/or removing application processes for the application and removing state information for the application from the memory of the device. Generally, opening a second application while in a first application does not close the first application. When the second application is displayed and the first application ceases to be displayed, the first application becomes a background application. 
       FIG. 5  illustrates an exemplary schematic block diagram of text-to-speech module  500  in accordance with some embodiments. In some embodiments, text-to-speech module  500  is implemented using one or more multifunction devices including but not limited to devices  100 ,  400 , and  1100  ( FIGS. 1A, 2, 4A -B, and  11 ). In particular, memory  102  ( FIG. 1A ) or  370  ( FIG. 3 ) can include text-to-speech module  500 . Text-to-speech module  500  can enable speech synthesis capabilities in a multifunctional device. Specifically, text-to-speech module  500  can enable a multifunctional device to perform the unit-selection text-to-speech synthesis processes (e.g., process  700 ) described herein. 
     As shown in  FIG. 5 , text-to-speech module  500  is configured to receive text to be converted to speech and output a speech waveform corresponding to the spoken form of the received text. The text is received by text analysis module  502  of text-to-speech module  500 . Text analysis module  502  is configured to convert the text into a sequence of target units representing the spoken pronunciation of the text. Notably, each target unit is not an actual speech unit. Rather each target unit is the linguistic specification of the desired unit according to the received text. The desired unit is a theoretical phonetic unit, such as a phone, diphone, half-phone, or the like. Each target unit specifies linguistic features (e.g., speech segment position, syllables, syllabic stress, syllable position, phrase length, part of speech, word prominence, context, etc.) that correspond to the text. In some examples, text analysis module  502  applies orthographic rules and grammar rules to convert the text into the sequence of target units. In other examples, text analysis module  502  includes a lexicon where words in text form are mapped to their corresponding target units. The sequence of target units with corresponding linguistic features is forwarded to unit-selection module  504 . 
     Speech segment database  508  includes a plurality of speech segments derived from recorded speech and a corresponding corpus of text. Each speech segment includes linguistic features and acoustic features (e.g., spectral shape, pitch, duration, Mel-frequency cepstral coefficients, fundamental frequency, etc.). The plurality of speech segments are indexed and stored in speech segment database  508  according to the linguistic features and acoustic features. The speech segments of speech segment database  508  are generated, for example, using process  1000  described below with reference to  FIG. 10 . 
     Unit-selection module  504  is configured to pre-select suitable speech segments from speech segment database  508  that best match the sequence of target units. In particular, unit-selection module  504  is configured to pre-select one or more candidate speech segments from speech segment database  508  for each target unit of the sequence of target units. The pre-selection is based on a determined cost that indicates how well the linguistic features of a particular candidate speech segment match with the linguistic features of the respective target unit. 
     Using one or more statistical models stored in acoustic feature prediction model(s)  506 , unit-selection module  504  is configured to determine predicted statistical parameters of acoustic features for each target unit of the sequence of target units. The predicted statistical parameters include, for example, the means, variances, or density weights of the acoustic features. The one or more statistical models are trained using recorded speech and a corresponding corpus of text. In some examples, the one or more statistical models include a mixture density network (e.g., mixture density network  900  of  FIG. 9 , described below). The linguistic features of a target unit are used to determine the predicted statistical parameters of acoustic features for the target unit. For example, the one or more statistical models receive the linguistic features of a target unit and determine corresponding predicted statistical parameters of the acoustic features for the target unit. 
     Unit-selection module  504  is configured to determine a target cost for a pre-selected candidate speech segment based on the predicted statistical parameters of a first acoustic feature of the acoustic features associated with the respective target unit. For example, as discussed in greater detail below with respect to block  710  of  FIG. 7 , the target cost is based on the weighted difference between the actual acoustic features of the pre-selected candidate speech segment and the predicted statistical parameters of the first acoustic feature associated with the respective target unit. Unit-selection module  504  is further configured to determine, for a pre-selected candidate speech segment, a plurality of concatenation costs with respect to a plurality of subsequent pre-selected candidate speech segments. In particular, the plurality of concatenation costs are determined based on the predicted statistical parameters of a second acoustic feature of the acoustic features associated with the respective target unit. As discussed in greater detail below with respect to block  712  of  FIG. 7 , the concatenation cost is based on the weighted difference between the actual acoustic features of the pre-selected candidate speech segment and the predicted statistical parameters of the second acoustic feature associated with the respective target unit. 
     Unit-selection module  504  is configured to select from the pre-selected candidate speech segments a subset of pre-selected candidate speech segments for speech synthesis. The selecting is based on a combined cost associated with the subset. The combined cost is determined based on the target cost and the plurality of concatenation costs of each pre-selected candidate speech segment. For example, unit-selection module  504  is configured to perform a Viterbi search through the pre-selected candidate speech segments to determine the subset of pre-selected candidate speech segments having the lowest combined cost. The selected subset is then used to synthesize speech corresponding to the received text. 
     Speech synthesizer module  510  is configured to receive the selected subset of pre-selected candidate speech segments from unit-selection module  504  and join the sequence of speech segments into a continuous speech waveform. Speech synthesizer module  510  is further configured to apply various signal processing algorithms to smooth out the acoustic features between speech segments to generate a smooth, continuous speech waveform. The speech waveform is an audio rendering of the spoken form of the text received at text analysis module  502 . In particular, the speech waveform is in the form of an audio signal or audio data file (e.g., .wav, .mp3, .wma, etc.). 
       FIG. 6  illustrates an exemplary block diagram of speech segment generation module  600  in accordance with some embodiments. In some embodiments, speech segment generation module  600  is implemented using one or more multifunction devices including but not limited to devices  100 ,  400 , and  1100  ( FIGS. 1A, 2, 4A -B, and  11 ). In particular, memory  102  ( FIG. 1A ) or  370  ( FIG. 3 ) includes speech segment generation module  600 . As shown in  FIG. 6 , speech segment generation module  600  includes language model generation module  602 , automatic speech recognition module  604 , verification module  606 , feature generation module  608 , and voice building module  610 . Speech segment generation module  600  can enable the generation of speech segments for a speech segment database (e.g., speech segment database  508 ) in a multifunctional device. Specifically, speech segment generation module  600  is used to perform process  1000  for generating a database of speech segments for use in unit-selection text-to-speech synthesis, described below. 
     Language model generation module  602  is configured to receive a corpus of text and generate a language model. The generated language model is configured to predict a current word given a context of previous words. For example, the generated language model is an n-gram language model. In some examples, the generate language model is a statistical language model or a neural network based language model. 
     Automatic speech recognition module  604  is configured to receive speech input and generate speech recognition results corresponding to the speech input. In particular, the speech recognition results include text corresponding to the speech input. Automatic speech recognition module  604  includes a front-end speech pre-processor for extracting representative features from the speech input. For example, the front-end speech pre-processor can perform a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors. Further, automatic speech recognition module  604  includes one or more speech recognition models (e.g., acoustic models and/or language models) and can implement one or more speech recognition engines. Examples of speech recognition models include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of speech recognition engines include the dynamic time warping based engines and weighted finite-state transducers (WFST) based engines. The one or more speech recognition models and the one or more speech recognition engines are used to process the extracted representative features of the front-end speech pre-processor to produce intermediate recognitions results (e.g., phonemes, phonemic strings, and sub-words), and ultimately, speech recognition results (e.g., words, word strings, or sequence of tokens). 
     Verification module  606  is configured to compare the speech recognition results (e.g., from automatic speech recognition module  604 ) with a reference corpus of text to identify any mismatches. Verification module  606  is configured to extract out the portions of the reference corpus of text where the speech recognition results do not match the reference corpus of text. Further, verification module  606  is configured to extract out portions of recorded speech corresponding to the extracted portions of the reference corpus of text. Verification module  606  then sends out the portions of the reference corpus of text and the corresponding portions of recorded speech to be verified and/or corrected by a separate verification service (e.g., a crowdsourcing service). Verification module  606  is further configured to receive corrected portions of speech recognition results and corrected portions of recorded speech from the separate verification service. Verification module  606  generates verified recorded speech and a verified corpus of text by modifying the recorded speech and/or the reference corpus of text based on the received corrected portions of the corpus of text and corrected portions of recorded speech. 
     Returning back to automatic speech recognition module  604 , automatic speech recognition module  604  is configured to process the verified recorded speech from verification module  606 . The verified recorded speech is separated into a plurality of speech segments (e.g., phones or sub-phones). Automatic speech recognition module  604  further processes the verified corpus of text of the recorded speech to force-align the verified recorded speech to the verified corpus of text. Each speech segment thus corresponds to an aligned portion of the corpus of text. 
     Feature generation module  608  is configured to analyze each speech segment of the verified recorded speech to determine the acoustic features associated with the respective speech segment. For example, spectral shape, pitch, duration, Mel-frequency cepstral coefficients, fundamental frequency, or the like can be determine for each speech segment. In particular, feature generation module  608  is configured to determine the fundamental frequency of a speech segment. For example, several fundamental frequency estimation methods known in the art can be implemented in a voting scheme that forms a robust fundamental frequency curve. The fundamental frequency curve is then used in pitch marking to derive the pseudo-glottal closure instant locations. The fundamental frequency of a speech segment is determined based on the derived pseudo-glottal closure instant locations. 
     Voice building module  610  is configured to generate labeled speech segments. In particular, each speech segment generated from the verified recorded speech is labeled to indicate the linguistic features and acoustic features of the speech segment. The labeled speech segments are stored in an indexed speech segment database (e.g., speech segment database  508 ). The labeled speech segments are thus searched and retrieved based on their identity (e.g., the specific phone or sub-phone), their linguistic features, or their acoustic features. 
       FIG. 7  illustrates a flow diagram of an exemplary process  700  for unit-selection text-to-speech synthesis in accordance with some embodiments. Process  700  can be performed using one or more of devices  100 ,  300 , and  1100  ( FIGS. 1A, 2, 3A -B, and  11 ). In particular, process  700  can be performed using a text-to-speech module (e.g., text-to-speech module  500  of  FIG. 5 ), implemented on the one or more devices. It should be appreciated that some operations in process  700  can be combined, the order of some operations can be changed, and some operations can be omitted. 
     At block  702 , text to be converted to speech is received. In some examples, the text is received via user input (e.g., from a keyboard, touch screen, etc.). In other examples, the text is received from a digital assistant implemented on the electronic device. In particular, the digital assistant generates a text response to satisfy a user request. The text response is received from a remote digital assistant server or a local client digital assistant module. In yet other examples, the text is received from an application (e.g., application  136 ) of the electronic device. The text is in the form of a sequence of tokens representing the text. In an illustrative example shown in  FIG. 8 , the received text includes the word “closet.” 
     At block  704 , a sequence of target units representing a spoken pronunciation of the text is generated. The sequence of target units is generated using a text analysis module (e.g., text analysis module  502 ) of the device. In particular, the text is converted to the sequence of target units. The sequence of target units is a phonetic transcription or a phonemic transcription of the text. In the context of the present disclosure, “target units” are not actual speech units. Rather, the sequence of target units specifies a plurality of phonetic units that are arranged in an order consistent with the text. The sequence of target units thus represents the linguistic specifications of the desired units according to the text. Each target unit in the sequence of target units specifies linguistic features (also referred to as text features) corresponding to the respective portion of the text. In particular, the linguistic features include context (e.g., phone position, syllable position, phrase length, part of speech, etc.) extracted from the text. The linguistic features are extracted from the text by applying a set of predetermined rules, using a linguistic feature model, or using a database that can map words of the text to corresponding linguistic features. It should be recognized that the text may be pre-processed (e.g., cleaned and normalized) prior to converting the text to the sequence of target units. 
     In one example, depicted in  FIG. 8 , the text “closet” is converted to sequence of target units  802  “K1-K2-L1-L2-AA1-AA2-Z1-Z2-AH1-AH2-T1-T2,” where each target unit specifies a respective half-phone according to the text. Further, each target unit specifies linguistic features that are extracted from the text “closet.” In this example, sequence of target units  802  includes first target unit  804  (e.g., AA1) and second target unit  806  (e.g., AA2). First target unit  804  precedes second target unit  806  in sequence of target units  802 . In particular, first target unit  804  and second target unit  806  are consecutive target units where first target unit  804  immediately precedes second target unit  806  and no other target unit is disposed between first target unit  804  and second target unit  806 . The sequence of target units is represented mathematically as T={t 1 , t 2 , . . . t N }, where each target unit, t n , is a vector of the linguistic features corresponding to the respective target unit. Thus, in the present example, first target unit  804  is represented as the linguistic feature vector t 5  and second target unit  806  is represented as the linguistic feature vector t 6 . The linguistic feature vector of a target unit includes, for example, the 1-of-N coding of each half-phone, additional syllable, word, and sentence/phrase level features, and prominence/stress features. In a specific example, the length of each linguistic feature vector is 233. 
     At block  706 , predicted statistical parameters for each of a plurality of acoustic features associated with each target unit in the sequence of target units are determined. In particular, a trained statistical model is used to determine, based on the linguistic features corresponding to a target unit in the sequence of target units, the predicted statistical parameters for each of the plurality of acoustic features associated with the target unit. The statistical model is generated (e.g., trained) using recorded speech and a corresponding corpus of text. In some examples, the statistical model is configured to receive, as inputs, the linguistic features of a respective target unit (e.g., linguistic feature vector t 5  of first target unit  804 ). Based on the inputted linguistic features, the statistical model is configured to output the predicted statistical parameters for each of the plurality of acoustic features associated with the respective target unit (e.g., first target unit  804 ). Blocks  706 - 714  can be performed using a unit-selection module (e.g., unit-selection module  504 ) of the device. 
     In some examples, the predicted statistical parameters include a mean parameter for each of the plurality of acoustic features and a variance parameter for each of the plurality of acoustic features. Further, in some examples, the predicted statistical parameters include one or more density weights for each of the plurality of acoustic features associated with the respective target unit. In some examples, the plurality of acoustic features include Mel-frequency cepstral coefficients, fundamental frequency, pitch, or duration of the respective target unit. The plurality of acoustic features further include one or more acoustic features each representing a change (e.g., delta) in an acoustic feature. For example, the plurality of acoustic features include a second acoustic feature (e.g., delta fundamental frequency or delta mel-frequency cepstral coefficient) that represents a change in the first acoustic feature (e.g., fundamental frequency or mel-frequency cepstral coefficient) of the respective target unit. In some examples, the change in an acoustic feature is a slope of the acoustic feature. For example, the plurality of acoustic features include a slope of the pitch at the beginning or end of the respective target unit. 
     In some examples, any one of the plurality of acoustic features can correspond to a specific portion of the respective target unit. For example, one or more acoustic features of the plurality of acoustic features correspond to the beginning, the middle, or the end of the respective target unit. Thus, in one example, an acoustic feature of the plurality of acoustic features is the fundamental frequency at the beginning of the respective target unit, another acoustic feature of the plurality of acoustic features is the fundamental frequency at the middle of the respective target unit, and yet another acoustic feature of the plurality of acoustic features is the fundamental frequency at the end of the respective target unit. In another example, the plurality of acoustic features include a first plurality of mel-frequency cepstral coefficients at a beginning of the respective target unit, a second plurality of mel-frequency cepstral coefficients at a middle of the respective target unit, and a third plurality of mel-frequency cepstral coefficients at an end of the respective target unit. In yet another example, an acoustic feature of the plurality of acoustic features is the change in fundamental frequency at the end of the respective target unit or a change in the mel-frequency cepstral coefficient at the end of the respective target unit. 
     Acoustic features that represent a change in certain acoustic features (e.g., delta fundamental frequency or delta mel-frequency cepstral coefficients) can be desirable for predicting concatenation. For example, the predicted delta fundamental frequency at the end of first target unit  804  indicates whether the pitch at the end of this target unit is expected to go up or down and by how much. This information is then used to select (e.g., at block  714 ) a suitable pair of candidate speech units (e.g., first candidate speech unit  810  and second candidate speech unit  812 ) that concatenate in the expected manner. This can improve the accuracy and naturalness of the resultant synthesized speech as compared to methods where the difference in acoustic features between pairs of candidate speech segments are merely minimized without referencing a predicted concatenation parameter. 
     In some examples, the statistical model is a deep neural network composed by a mixture of probability distributions. In particular, the statistical model is a mixture density network or a recurrent mixture density network. With reference to  FIG. 9 , exemplary mixture density network  900  for determining predicted statistical parameters for each of a plurality of acoustic features associated with a respective target unit in the sequence of target units is depicted. Mixture density network  900  includes multiple layers. In particular, mixture density network  900  includes input layer  902 , output layer  904 , and one or more hidden layers  906  disposed between input layer  902  and output layer  904 . In this example, mixture density network  900  includes three hidden layers  906 . It should be recognized, however, that in other examples, mixture density network  900  can include any number of hidden layers  906 . 
     Each layer of mixture density network  900  includes multiple units. The units are the basic computational elements of mixture density network  900  and are referred to as dimensions, neurons, or nodes. As shown in  FIG. 9 , input layer  902  includes input units  908 , hidden layers  906  include hidden units  910 , and output layer  904  includes output units  912 . Hidden layers  906  each include any number of hidden units  910 . In a specific example, hidden layers  906  each include 512 hidden units  910 . The units are interconnected by connections  914 . Specifically, connections  914  connect the units of one layer to the units of a subsequent layer. Further, each connection  914  is associated with a weighting value and a bias followed by a nonlinear activation function. For simplicity, the weighting values and biases are not shown in  FIG. 9 . 
     Input layer  902  is configured to receive the linguistic features (e.g., linguistic feature vector t n ) associated with the respective target unit. The number of input units  908  in input layer  902  corresponds to the length of the linguistic feature vector of the respective target unit. Each input unit is configured to process a specific linguistic feature represented in the linguistic feature vector. In a specific example, input layer  902  includes 233 input units  908  to receive a linguistic feature vector having a length of 233. 
     Output layer  904  is configured to output the predicted statistical parameters for each of the plurality of acoustic features associated with the respective target unit. In particular, the outputted predicted statistical parameters for each of the plurality of acoustic features correspond to the linguistic features of the respective target unit received at input layer  902 . For example, output layer  904  outputs the predicted mean and variance of each acoustic feature associated with the respective target unit. Output layer  904  is further configured to output density weights for each acoustic feature associated with the respective target unit. In some examples, output layer  904  applies a likelihood function that is the linear combination of multiple densities, such as a Gaussian Mixture Model (GMM). In some examples, output layer  904  applies exponential activation functions for the portion of the output layer that generates the variances of acoustic features, and linear activation functions for the portion of the output layer that generates the means of acoustic features. 
     As discussed above, the plurality of acoustic features include one or more acoustic features, each representing a change in an acoustic feature at a specific portion of the respective target unit. Mixture density network  900  is thus configured to output, at output layer  904 , the predicted statistical parameters (e.g., mean and variance) for the change in an acoustic feature at a specific portion of the respective target unit. For example, mixture density network  900  is configured to output, at output layer  904 , the mean and variance of the change in fundamental frequency at the end of the respective target unit or the change in each of the mel-frequency cepstral coefficients (e.g., delta mel-frequency cepstral coefficient) at the end of the respective target unit. As discussed, determining the predicted change in one or more acoustic features at the end of a target unit can be desirable as a metric for selecting candidate speech segments that concatenate well, thereby improving the quality and naturalness of the synthesized speech. 
     It should be recognized that the predicted statistical parameters of a second acoustic feature of the plurality of acoustic features for the respective target unit may not be derived from the predicted statistical parameters of a first acoustic feature of the plurality of acoustic features for the respective target unit. For example, the predicted statistical parameters of the first acoustic feature for the respective target unit may not be used as a starting point to calculate the predicted statistical parameters of the second acoustic feature for the respective target unit. Rather, mixture density network  900  independently determines the predicted statistical parameters of the second acoustic feature for the respective target unit and the predicted statistical parameters of the first acoustic feature for the respective target unit. For example, mixture density network  900  is configured to independently determine the predicted statistical parameters of the delta fundamental frequency at the end of the respective target unit and the predicted statistical parameters of the fundamental frequency at the end of the respective target unit. 
     Mixture density network  900  is trained based on data that includes recorded speech and a corresponding corpus of text. In some examples, mixture density network  900  is trained in parallel using multiple CPUs. The parallel training scheme can search for an optimal weight space and provide a model faster than sequential training. This model is further retrained on the whole of the data to obtain the final mixture density network that is used at block  706  to determine the predicted statistical parameters for each of a plurality of acoustic features associated with a respective target unit. 
     At block  708 , a plurality of candidate speech segments corresponding to the sequence of target units are selected based on the linguistic features of each target unit. In particular, the plurality of candidate speech segments are selected from a database of speech segments (e.g., database of speech segments  508 ). The database of speech segments is generated from recorded speech corresponding to a corpus of text. Thus, each candidate speech segment of the plurality of candidate speech segments is a segment (e.g., speech unit, phone, diphone, half-phone, etc.) of the recorded speech. Further, each speech segment includes actual linguistic features (e.g., speech segment position, syllables, syllabic stress, syllable position, phrase length, part of speech, word prominence, etc.) and actual acoustic features (e.g., spectral shape, pitch, duration, Mel-frequency cepstral coefficients, fundamental frequency, etc.). The actual acoustic features of a given candidate speech segment can be represented by a vector x. Additional details of how the database of speech segments is generated are provided below with reference to  FIG. 10 . 
     With reference to  FIG. 8 , candidate speech segments  808  corresponding to sequence of target units  802  is selected from the database of speech segments. The selection of candidate speech segments  808  is based on the linguistic features of each target unit in the sequence of target units  802 . Specifically, for each target unit, the database of speech segments is searched to find a corresponding set of candidate speech segments having actual linguistic features that closely match (e.g., a target score that is greater than a predetermined value) the linguistic features of the respective target unit. In the present example shown in  FIG. 8 , candidate speech segments  808  include a corresponding set of candidate speech segments selected for each target unit. For example, candidate speech segments  808  include five candidate speech segments  809  (including first candidate speech segment  810 ) selected for first target unit  804  based on the linguistic features of first target unit  804 . Candidate speech segments  808  also include four candidate speech segments  811  (including second candidate speech segment  812 ) selected for second target unit  806  based on the linguistic features of second target unit  806 . 
     At block  710 , a target cost is determined for each candidate speech segment of the plurality of candidate speech segments based on the predicted statistical parameters of a first acoustic feature of the plurality of acoustic features associated with a respective target unit of the sequence of target units. For example, with reference to  FIG. 8 , a target cost is calculated for each of candidate speech segments  808  with respect to the corresponding target unit. Specifically, first target unit  804  is associated with mean and variance parameters of one or more acoustic features (e.g., fundamental frequency, mel-frequency cepstral coefficients, delta fundamental frequency, delta mel-frequency cepstral coefficients, duration, etc.) that were determined at block  706 . A target cost is determined for first candidate speech segment  810  based on the mean and variance parameters of the one or more acoustic features associated with first target unit  804 . Similarly, second target unit  806  is associated with separate mean and variance parameters of one or more acoustic features (e.g., fundamental frequency, mel-frequency cepstral coefficients, delta fundamental frequency, delta mel-frequency cepstral coefficients, duration, etc.) that were determined at block  706 . A target cost is determined for second candidate speech segment  812  based on the mean and variance parameters of the one or more acoustic features associated with second target unit  806 . 
     The target cost for a candidate speech segment indicates how close the actual acoustic features of the candidate speech segment match with the predicted acoustic features of the respective target unit. In some examples, a lower target cost indicates a closer match between the actual acoustic features of the candidate speech segment to the predicted acoustic features of the respective target unit. In some examples, the target cost for each candidate speech segment  808  is the product of Gaussian densities determined using equation (1) shown below. In other examples, in order to achieve a better spacing and resolution, the target cost is the weighted Gaussian negative log-likelihoods determined using equation (2) shown below. 
                   C   =       ∏   i     ⁢           ⁢       w   i     ⁢     1       √   2     ⁢           ⁢   π   ⁢           ⁢     σ   2         ⁢   exp   ⁢     {     -         (       x   i     -     μ   i       )     2       2   ⁢           ⁢     σ   i   2           }                 (   1   )               C   =       ∑   i     ⁢           ⁢       w   i     ⁢         (       x   i     -     μ   i       )     2       2   ⁢           ⁢     σ   i   2                     (   2   )               
In equations (1) and (2), C is the cost, i is the acoustic feature index, w i  is a weighting value associated with the respective acoustic feature, x i  is the actual acoustic feature of the speech segment, μ i  is the mean of the acoustic feature of the respective target unit, and σ i   2  is the variance of the acoustic feature of the respective target unit. In a specific example, the target cost is based on the mean and variance of the fundamental frequency at one or more portions of the respective target unit and the duration of the respective target unit. In this example, the target cost defines the prosody of the speech segments.
 
     As indicated in equations (1) and (2), the target cost for a respective candidate speech segment is based on (x i −μ i ), which is the difference between the actual value of an acoustic feature (x i ) for the respective candidate speech segment and the predicted mean of the acoustic feature for the respective target unit. This difference (x i −μ i ) is weighted by the variance (σ i   2 ) of the first acoustic feature for the respective target unit. Thus, the target cost for a respective candidate speech segment is based on the weighted difference (x i −μ i ) 2 /2σ i   2 . Weighting the difference with the variance (α i   2 ) brings the cost into the probabilistic domain, which results in a more meaningful comparison between the candidate speech segment and the respective target unit. In particular, the target cost for a candidate speech segment represents the likelihood of the candidate speech segment given the acoustic features of the candidate speech segment. The candidate speech segments selected at block  714 , based on the target cost for speech synthesis, can thus be more accurate, thereby resulting in more natural sounding speech. 
     At block  712 , a plurality of concatenation costs for each candidate speech segment of the plurality of candidate speech segments are determined with respect to a plurality of subsequent candidate speech segments. The plurality of concatenation costs are determined based on the predicted statistical parameters of a second acoustic feature of the plurality of acoustic features associated with the respective target unit of the sequence of target units. For example, each concatenation cost is based on the mean and variance of the delta fundamental frequency (delta pitch) and/or the delta mel-frequency cepstral coefficients at a specific portion of the respective target unit (e.g., at the end of the respective target unit). 
     Returning to the example of  FIG. 8 , concatenation costs are determined for each of candidate speech segments  808  with respect to one or more subsequent candidate speech segments of candidate speech segment  808 . Specifically, for first candidate speech segment  810 , a concatenation cost is determined for each subsequent candidate speech segment (e.g., candidate speech segments  811 ) corresponding to the subsequent target unit (e.g., second target unit  806 ). Thus, for first candidate speech segment  810 , separate concatenation costs are determined with respect to each of candidate speech segments  811 . Therefore, every connection (e.g., connection  814  or  817 ) joining every consecutive pair of candidate speech segments (first candidate speech segment  810  and second candidate speech segment  812 ) in candidate speech segments  808  is associated with a concatenation cost. 
     The concatenation cost for a candidate speech segment with respect to a subsequent candidate speech segment indicates how close the actual concatenation of the pair of candidate speech segments matches with the predicted concatenation of the respective target unit with respect to the subsequent target unit. In some examples, a lower concatenation cost indicates a closer match between the actual concatenation of the candidate speech segment with the subsequent candidate speech segment and the predicted concatenation of the respective target unit with the subsequent target unit. 
     As discussed above, first target unit  804  is associated with the means and variances of one or more acoustic features (e.g., fundamental frequency, mel-frequency cepstral coefficients, delta fundamental frequency, delta mel-frequency cepstral coefficients, duration, etc.) that were determined at block  706 . The concatenation costs determined for first candidate speech segment  810  are based on the means and variances of the one or more acoustic features associated with first target unit  804 . Similarly, second target unit  806  is associated with means and variances of one or more acoustic features (e.g., fundamental frequency, mel-frequency cepstral coefficients, delta fundamental frequency, delta mel-frequency cepstral coefficients, duration, etc.) that were determined at block  706 . The concatenation costs determined for second candidate speech segment  812  are based on the means and variances of the one or more acoustic features associated with second target unit  806 . 
     In some examples, each concatenation cost is the product of Gaussian densities determined using equation (1) described above or the weighted Gaussian negative log-likelihoods determined using equation (2) described above. Similar to the target cost, the concatenation cost for a candidate speech segment with respect to a subsequent candidate speech segment is based on (x i −μ i ), which is the difference between the actual value of an acoustic feature (x i ) for the candidate speech segment with respect to the subsequent candidate speech segment and the predicted mean of the acoustic feature for the respective target unit. In one example, the actual value of the acoustic feature for the candidate speech segment with respect to the subsequent candidate speech segment is the difference between an actual value of the first acoustic feature at an end of the candidate speech segment and an actual value of the first acoustic feature at a beginning of the subsequent candidate speech segment. For example, the concatenation cost for first candidate speech segment  810  with respect to second candidate speech segment  812  is based on the difference between the actual delta fundamental frequency at the end of first candidate speech segment  810  and the predicted mean of the delta fundamental frequency at the end of first target unit  804 . The actual delta fundamental frequency at the end of first candidate speech segment  810  is the difference between the actual fundamental frequency at the end of first candidate speech segment  810  and the actual fundamental frequency at the beginning of second candidate speech segment  812 . 
     Further, the difference (x i −μ i ) is weighted by the variance (σ 2 ) of the first acoustic feature for the respective target unit. For example, the difference between the actual delta fundamental frequency at the end of first candidate speech segment  810  and the predicted mean of the delta fundamental frequency at the end of first target unit  804  is weighted by the predicted variance of the delta fundamental frequency at the end of first target unit  804 . Thus, the concatenation cost for a respective candidate speech segment is based on the weighted difference (x i −μ i ) 2 /2σ i   2 . As discussed above, weighting the difference with the variance (σ i   2 ) brings the cost into the probabilistic domain, which results in a more meaningful comparison between the candidate speech segment and the respective target unit. In particular, the concatenation cost for a pair of candidate speech segments represents the likelihood of the subsequent candidate speech segment succeeding the candidate speech segment given the acoustic parameters of the candidate speech segment with respect to the subsequent candidate speech segment. The candidate speech segments selected based on the concatenation cost at block  714  for speech synthesis can thus be more accurate, thereby resulting in more natural sounding speech. 
     At block  714 , a subset of candidate speech segments is selected from the plurality of candidate speech segments for speech synthesis. The selecting at block  714  is based on a combined cost associated with the subset of candidate speech segments. The combined cost is determined based on the target costs of each candidate speech segment (determined at block  710 ) and the concatenation costs of each candidate speech segment with respect to subsequent candidate speech segments (determined at block  712 ). 
     The selecting of the subset of candidate speech segments is based on a Viterbi search to determine the sequence of candidate speech segments having the lowest combined cost. For example, with reference to  FIG. 8 , candidate speech segments  808  form a Viterbi search lattice where each candidate speech segment is associated with a target cost and each connection between pairs of consecutive speech segments is associated with a concatenation cost. Each path through the Viterbi search lattice represents a possible sequence of candidate speech segments that can be joined to synthesize the phrase “closet.” Further, each path is associated with a combined cost that is based on the target costs of the candidate speech segments and the concatenation costs of the corresponding connections associated with the respective path. In some examples, different weighting factors are applied to the target costs and the concatenation costs to determine the combined cost for a given path through the Viterbi search lattice. The path associated with the lowest combined cost is selected and the sequence of candidate speech segments corresponding to the selected path is used to synthesize speech. For example, in FIG.  8 , path  820  indicated in bold is determine to have the lowest combined cost among all the possible paths through the Viterbi search lattice and thus the sequence of candidate speech segments associated with path  820  is selected for speech synthesis at block  714 . 
     At block  716 , speech corresponding to the received text is generated using the subset of candidate speech segments. For example, the sequence of candidate speech segment corresponding to path  820  in  FIG. 8  can be joined together to form a continuous speech waveform representing the spoken form of the received text “closet.” In addition, various signal processing methods known in the art can be implemented to achieve a smooth speech audio waveform. In some examples, the generated speech is in the form of an audio signal representing the spoken form of the text received at block  702 . Alternatively, the generated speech is an audio file (e.g., .wav, .mp3, .wma, etc.) representing the spoken form of the text received at block  702 . In some examples, the generated speech is outputted to the user. For example, the generated speech at block  716  is outputted via a speaker (e.g., speaker  111 ) of the device. 
       FIG. 10  illustrates a flow diagram of exemplary process  1000  for generating a database of speech segments for use in unit-selection text-to-speech synthesis in accordance with some embodiments. Process  1000  can be performed using one or more of devices  100 ,  300 , and  1100  ( FIGS. 1A, 2, 3A -B, and  11 ). In particular, process  1000  can be performed using a speech segment generation module (e.g., speech segment generation module  600  of  FIG. 6 ), implemented on the one or more devices. It should be appreciated that some operations in process  1000  can be combined, the order of some operations can be changed, and some operations can be omitted. 
     At block  1002 , recorded speech corresponding to a corpus of text is obtained. The recorded speech is spoken by a single person, such as a voice talent. Specifically, the recorded speech is a reading of the corpus of text by the voice talent. In some examples, the recorded speech contains several hours (e.g., 3-5 hours or 5-10 hours) of recorded speech. The recorded speech includes some deviations from the corpus of text. Allowing for deviations enables the voice talent to read the corpus of text in a more natural manner, which results in more natural-sounding speech segments for speech synthesis. 
     At block  1004 , a custom language model is built from the corpus of text. The language model is, for example, an n-gram language model. Block  1004  is performed by a language model generator module (e.g., language model generation module  602 ). By training the language model using the corpus of text itself, the language model is optimized for determining words and phrases found in the corpus of text. 
     At block  1006 , speech-to-text conversion of the recorded speech is performed using the language model of block  1004  to obtain speech recognition results corresponding to the recorded speech. Block  1006  can be performed using an automatic speech recognition module (e.g., automatic speech recognition module  604 ). Because the language model is trained using the corpus of text, the accuracy of the speech recognition results is improved as compared to using a generic language model trained using a general corpus of text. 
     At block  1008 , portions of the corpus of text where the speech recognition results do not match with the corpus of text are extracted out. In particular, the speech recognition results are compared to the corpus of text to identify any mismatches. Mismatches include any portion of the speech recognition results having different words, missing words, or added words with respect to the corpus of text. Mismatches also include words in the speech recognition results associated with a poor confidence score (e.g., lower than a predetermined threshold). The portions of the corpus of text that correspond to the mismatches of the speech recognition results are extracted out. Further, at block  1010 , portions of recorded speech that correspond to the extracted portions of the corpus of text in block  1008  are extracted out from the recorded speech. The collection of portions of the corpus of text and corresponding portions of recorded speech obtained at blocks  1008  and  1010  is stored. Blocks  1008  and  1010  can be performed using a verification module (e.g., verification module  606 ). 
     At block  1012 , corrected portions of the corpus of text and corrected portions of recorded speech are received. The corrected portions of the corpus of text and the corrected portions of recorded speech are based on the portions of the corpus of text and corresponding portions of recorded speech obtained at blocks  1008  and  1010 . For example, the portions of the corpus of text and corresponding portions of recorded speech obtained at blocks  1008  and  1010  are sent to a crowdsourcing service to correct and/or verify each portion of recorded speech with the corresponding portion of the corpus of text. In these examples, the corrected portions of the corpus of text and the corrected portions of recorded speech are received from the crowdsourcing service. Other methods can alternatively be implemented to correct and/or verify the portions of the corpus of text and the corresponding portions of recorded speech. For example, the corresponding portions of recorded speech are processed using more robust speech-to-text algorithms and models, and the results are compared to the corresponding portions of the corpus of text. 
     By verifying only the portions of the corpus of text and recorded speech where the speech recognition results do not match with the corpus of text (rather than the entire corpus of text and/or the entire recorded speech), the recorded speech and corpus of text are verified more quickly and efficiently. The recorded speech and/or the corpus of text are modified (e.g., using verification module  606 ) based on the corrected portions of speech recognition results and the corrected portions of recorded speech to obtain verified recorded speech and a verified corpus of text. 
     At block  1014 , labeled speech segments are generated based on the recorded speech, the corpus of text, the corrected portions of the corpus of text, and the corrected portions of recorded speech. In particular, the label speech segments are generated based on the verified recorded speech and the verified corpus of text of block  1012 . 
     For example, the verified recorded speech and the verified recorded speech are processed (e.g., using automatic speech recognition module  604 ) to force-align the verified recorded speech to the verified corpus of text and segment the verified recorded speech into speech segments (e.g., speech segments, phones, sub-phones, etc.). Each of the speech segments is labeled (e.g., using voice building module  610 ) to indicate the identity of the speech segment (e.g., the particular phone or sub-phone) and the linguistic features associated with the speech segment. Further, each speech segment is analyzed (e.g., using feature generation module  608 ) to determine the acoustic features associated with the respective speech segment. The determined acoustic features include, for example, fundamental frequency, mel-frequency cepstral coefficient, pitch, duration, or the like. In particular, determining the fundamental frequency of a speech segment can require pitch extraction processes. In some examples, several fundamental frequency estimation methods known in the art are implemented in a voting scheme that forms a robust fundamental frequency curve. The fundamental frequency curve is used in pitch marking to derive the pseudo-glottal closure instant locations. The fundamental frequency of a speech segment is thus determined based on the derived pseudo-glottal closure instant locations. 
     Each speech segment is labeled (e.g., using voice building module  610 ) to indicate the acoustic features of the speech segment. At block  1016 , the labeled speech segments of block  1014  are stored in an indexed speech segment database (e.g., speech segment database  508 ). Speech segments are thus searched and retrieved based on their identity (e.g., the specific phone or sub-phone), their linguistic features, or their acoustic features. 
     In accordance with some embodiments,  FIG. 11  shows a functional block diagram of an electronic device  1100  configured in accordance with the principles of the various described embodiments, including those described with reference to  FIG. 7 . The functional blocks of the device are, optionally, implemented by hardware, software, or a combination of hardware and software to carry out the principles of the various described embodiments. It is understood by persons of skill in the art that the functional blocks described in  FIG. 11  are, optionally, combined or separated into sub-blocks to implement the principles of the various described embodiments. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein. 
     As shown in  FIG. 11 , electronic device  1100  includes input unit  1103  configured to receive user input, such as text input, speaker unit  1104  configured to output speech, and communication unit  1106  configured to send and receive information (e.g., text) from external devices via a network. In some examples, electronic device  1100  optionally includes a display unit  1102  configured to display objects or text and receive touch/gesture input. Electronic device  1100  further includes processing unit  1108  coupled to input unit  1103 , speaker unit  1104 , communication unit  1106 , and optionally display unit  1102 . In some examples, processing unit  1108  includes receiving unit  1110 , generating unit  1112 , selecting unit  1114 , and determining unit  1116 . 
     In accordance with some embodiments, processing unit  1108  is configured to receive (e.g., with receiving unit  1110 ) text to be converted to speech. The text is received via one of display unit  1102 , input unit  1103 , or communication unit  1106 . Processing unit  1108  is further configured to generate (with generating unit  1112 ) a sequence of target units representing a spoken pronunciation of the text. Processing unit  1108  is further configured to determine (e.g., with determining unit  1116 , based on a plurality of linguistic features associated with each target unit of the sequence of target units, predicted statistical parameters for each of a plurality of acoustic features associated with each target unit. Processing unit  1108  is further configured to select (e.g., with selecting unit  1114 ), based on the plurality of linguistic features associated with each target unit, a plurality of candidate speech segments corresponding to the sequence of target units. Processing unit  1108  is further configured to determine (e.g., with determining unit  1116 ) a target cost for each candidate speech segment of the plurality of candidate speech segments, based on the predicted statistical parameters of a first acoustic feature of the plurality of acoustic features associated with a respective target unit of the sequence of target units. Processing unit  1108  is further configured to determine (e.g., with determining unit  1116 ) a plurality of concatenation costs with respect to a plurality of subsequent candidate speech segments for each candidate speech segment of the plurality of candidate speech segments. The plurality of concatenation costs is determined (e.g., with determining unit  1116 ) based on the predicted statistical parameters of a second acoustic feature of the plurality of acoustic features associated with the respective target unit of the sequence of target units. Processing unit  1108  is further configured to select (e.g., with selecting unit  1114 ) from the plurality of candidate speech segments a subset of candidate speech segments for speech synthesis. The selecting (with selecting unit  1114 ) is based on a combined cost associated with the subset of candidate speech segments. The combined cost is determined based on the target cost and the plurality of concatenation costs of each candidate speech segment. Processing unit  1108  is further configured to generate (e.g., with generating unit  1112 ) speech corresponding to the received text using the subset of candidate speech segments. 
     In some examples, the second acoustic feature represents a change of the first acoustic feature. In some examples, the change of the first acoustic feature is with respect to an end of the respective target unit. In some examples, the first acoustic feature comprises pitch and the second acoustic feature comprises a change in the pitch at an end of the respective target unit. In some examples, the first acoustic feature comprises a mel-frequency cepstral coefficient and the second acoustic feature comprises a change in the mel-frequency cepstral coefficient at an end of the respective target unit. In some examples, the plurality of acoustic features includes a pitch at a first portion of the respective target unit and a pitch at a second portion of the respective target unit. In some examples, the plurality of acoustic features includes a first plurality of mel-frequency cepstral coefficients at a first portion of the respective target unit and a second plurality of mel-frequency cepstral coefficients at a second portion of the respective target unit. In some examples, the plurality of acoustic features includes a duration of the respective target unit. 
     In some examples, the predicted statistical parameters of the second acoustic feature are not derived from the predicted statistical parameters of the first acoustic feature. In some examples, the predicted statistical parameters for each of the plurality of acoustic features include a mean parameter for each of the plurality of acoustic features and a variance parameter for each of the plurality of acoustic features. 
     In some examples, the target cost for a respective candidate speech segment is based on a weighted difference between an actual value of the first acoustic feature for the respective candidate speech segment and a first predicted statistical parameter of the predicted statistical parameters of the first acoustic feature for the respective target unit. The weighted difference is weighted by a second predicted statistical parameter of the predicted statistical parameters of the first acoustic feature for the respective target unit. 
     In some examples, a concatenation cost of the plurality of concatenation costs for a respective candidate speech segment includes a second weighted difference between an actual value of the second acoustic feature for the respective candidate speech segment with respect to a subsequent candidate speech segment of the plurality of subsequent candidate speech segments and a first predicted statistical parameter of the predicted statistical parameters of the second acoustic feature for the respective target unit, and wherein the second weighted difference is weighted by a second predicted statistical parameter of the predicted statistical parameters of the second acoustic feature for the respective target unit. 
     In some examples, the actual value of the second acoustic feature for the respective candidate speech segment with respect to the subsequent candidate speech segment of the plurality of subsequent candidate speech segments comprises a difference between an actual value of the first acoustic feature at an end of the respective candidate speech segment and an actual value of the first acoustic feature at a beginning of the subsequent candidate speech segment. In some examples, the plurality of candidate speech segments each comprise a segment of recorded speech. 
     In some examples, the predicted statistical parameters for each of the plurality of acoustic features associated with each target unit are determined using a statistical model. In some examples, the statistical model is composed by a mixture of probability distributions. 
     In some examples, the statistical model is configured to receive, as inputs, the plurality of linguistic features associated with a respective target unit and to output the predicted statistical parameters for each of the plurality of acoustic features associated with the respective target unit. The statistical model is further configured to output one or more density weights for each of the plurality of acoustic features associated with the respective target unit. 
     In some examples, the statistical model is a mixture density network comprising an input layer configured to receive as inputs the plurality of linguistic features associated with a respective target unit, an output layer configured to output the predicted statistical parameters for each of the plurality of acoustic features associated with the respective target unit, and at least one hidden layer between the input layer and the output layer. In some examples, the mixture density network is a recurrent mixture density network. 
     In some examples, the statistical model is configured to determine, for each target unit, the predicted statistical parameters of the second acoustic feature independent of the predicted statistical parameters of the first acoustic feature. In some examples, the statistical model is generated based on recorded speech corresponding to a corpus of text. 
     In some examples, the plurality of candidate speech segments is selected from a collection of speech segments. Processing unit  1108  is further configured to generate (e.g., with generating unit  1112 ) the collection of speech segments. In some examples, generating unit  1112  is further configured to obtain recorded speech corresponding to a corpus of text. Generating unit  1112  is further configured to generate a language model from the corpus of text. Generating unit  1112  is further configured to perform speech-to-text conversion of the recorded speech using the language model to obtain speech recognition results corresponding to the recorded speech. Generating unit  1112  is further configured to extract portions of the corpus of text where the speech recognition results do not match with the corpus of text. Generating unit  1112  is further configured to extract portions of recorded speech corresponding to the portions of the corpus of text. Generating unit  1112  is further configured to receive corrected portions of the corpus of text and corrected portions of the recorded speech. The corrected portions of the corpus of text and the corrected portions of the recorded speech are based on the portions of the corpus of text and the portions of recorded speech. Generating unit  1112  is further configured to generate labeled speech segments based on the recorded speech, the corpus of text, the corrected portions of the corpus of text, and the corrected portions of the recorded speech. The collection of speech segments is generated from the labeled speech segments. 
     In accordance with some implementations, a computer-readable storage medium (e.g., a non-transitory computer readable storage medium) is provided, the computer-readable storage medium storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods described herein. 
     In accordance with some implementations, an electronic device (e.g., a multifunctional device) is provided that comprises means for performing any of the methods described herein. 
     In accordance with some implementations, an electronic device (e.g., a multifunctional device) is provided that comprises a processing unit configured to perform any of the methods described herein. 
     In accordance with some implementations, an electronic device (e.g., a multifunctional device) is provided that comprises one or more processors and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods described herein. 
     The operation described above with respect to  FIG. 7  is, optionally, implemented by components depicted in  FIGS. 1A-B ,  3 ,  5 , and  11 . For example, receiving operation  702  and generating operation  704  can be implemented by text analysis module  502 . Selecting operations  708 ,  714  and determining operations  706 ,  710 ,  712  can be implemented by unit-selection module  504 , acoustic feature prediction model(s)  506 , and speech segment database  508 . Generating operation  716  can be implemented by speech synthesizer module  510 . It would be clear to a person of ordinary skill in the art how other processes can be implemented based on the components depicted in  FIGS. 1A-B ,  3 ,  5 , and  11 . 
     It is understood by persons of skill in the art that the functional blocks described in  FIG. 11  are, optionally, combined or separated into sub-blocks to implement the principles of the various described embodiments. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein. For example, processing unit  1108  can have an associated “controller” unit that is operatively coupled with processing unit  1108  to enable operation. This controller unit is not separately illustrated in  FIG. 11  but is understood to be within the grasp of one of ordinary skill in the art who is designing a device having a processing unit  1108 , such as device  1100 . As another example, one or more units, such as receiving unit  1110 , may be hardware units outside of processing unit  1108  in some embodiments. The description herein thus optionally supports combination, separation, and/or further definition of the functional blocks described herein. 
     Executable instructions for performing the functions and processes described herein are, optionally, included in a non-transitory computer-readable storage medium or other computer program product configured for execution by one or more processors. Executable instructions for performing these functions are, optionally, included in a transitory computer-readable storage medium or other computer program product configured for execution by one or more processors. 
     Although the disclosure and examples have been fully described with reference to the accompanying figures, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the appended claims.

Metadata:
Filing Date: 20160915
Publication Date: 20180403
Grant Date: 20180403
Priority Date: 20160526
Inventors: RAITIO Tuomo J.
PRAHALLAD KISHORE SUNKESWARI
CONKIE ALISTAIR D.
GOLIPOUR LADAN
WINARSKY DAVID A.
Assignee: APPLE INC
CPC Classifications: [{"code": "G10L13/10", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L13/06", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L13/0335", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L13/10", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L13/06", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L13/0335", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L13/10", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L13/07", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 60411516