Source: https://patents.google.com/patent/US8996377B2/en
Timestamp: 2020-04-06 04:59:19
Document Index: 557811968

Matched Legal Cases: ['art 212', 'art 214', 'art 216', 'arts 212', 'art 310', 'art 320', 'art 330', 'art 310', 'art 310', 'art 330', 'art 330', 'art 320', 'art 310', 'art 320', 'art 310', 'art 300', 'art.\n4', 'art.\n7', 'art.\n11', 'art.\n16']

US8996377B2 - Blending recorded speech with text-to-speech output for specific domains - Google Patents
Blending recorded speech with text-to-speech output for specific domains Download PDF
US8996377B2
US8996377B2 US13/547,459 US201213547459A US8996377B2 US 8996377 B2 US8996377 B2 US 8996377B2 US 201213547459 A US201213547459 A US 201213547459A US 8996377 B2 US8996377 B2 US 8996377B2
US13/547,459
US20140019134A1 (en
Difei Gao
2012-07-12 Application filed by Microsoft Technology Licensing LLC filed Critical Microsoft Technology Licensing LLC
2012-07-12 Priority to US13/547,459 priority Critical patent/US8996377B2/en
2012-08-03 Assigned to MICROSOFT CORPORATION reassignment MICROSOFT CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: LEUNG, MAX, DING, BINGGONG, GAO, DIFEI, WANG, PENG, Wu, Yijian, YE, SHENGHUA, ZHAO, SHENG
2014-01-16 Publication of US20140019134A1 publication Critical patent/US20140019134A1/en
2015-03-31 Publication of US8996377B2 publication Critical patent/US8996377B2/en
238000002156 mixing Methods 0 title claims description 20
230000003068 static Effects 0 claims abstract description 58
A text-to-speech (TTS) engine combines recorded speech with synthesized speech from a TTS synthesizer based on text input. The TTS engine receives the text input and identifies the domain for the speech (e.g. navigation, dialing, . . . ). The identified domain is used in selecting domain specific speech recordings (e.g. pre-recorded static phrases such as “turn left”, “turn right” . . . ) from the input text. The speech recordings are obtained based on the static phrases for the domain that are identified from the input text. The TTS engine blends the static phrases with the TTS output to smooth the acoustic trajectory of the input text. The prosody of the static phrases is used to create similar prosody in the TTS output.
Text-to-Speech (TTS) systems are becoming increasingly popular. The TTS systems are used in many different applications such as navigation, voice activated dialing, help systems, banking and the like. In many TTS applications, high quality recorded speech is used for specific prompts (e.g. turn left, turn right . . . ) that are specific to the application. This recorded speech is then combined with output from a TTS synthesizer according to definitions provided by a developer. Combining the recorded speech with output from the TTS system can be time consuming and difficult.
FIG. 1 illustrates an exemplary objective intelligibility assessment Text-To-Speech (TTS) system;
FIG. 2 shows splitting a non-uniform unit into different parts;
FIG. 3 shows blending in statistical parametric text-to-speech;
FIG. 4 shows an illustrative process for blending recorded speech with text-to-speech for specific domains;
FIG. 5 illustrates an exemplary system for blending recorded speech with TTS; and
FIGS. 6-8 and the associated descriptions provide a discussion of a variety of operating environments in which embodiments of the invention may be practiced.
FIG. 1 illustrates an exemplary objective intelligibility assessment Text-To-Speech (TTS) system. As illustrated, system 100 includes input text 105, TTS engine 110, voice data 130 and blended speech output 140. TTS engine 110 includes speech manager 120, domain detector 122, static phrase detector 124, blending unit 126 and TTS synthesizer 128.
Speech manager 120 is configured to combine recorded speech with synthesized speech based on input text. The speech manager 120 illustrated in TTS engine 110 receives input text 105 and identifies the domain for the speech using domain detector 122. Generally, a domain is specific to a type of application (e.g. navigation, dialing, virtual assistants, and the like . . . ). Each domain typically accesses specific recordings from one or more data stores that includes definitions of the recording units for high frequent phrases and associated waveform data, acoustic trajectory data, and the like. The speech data for each domain may be stored in one or more data stores, such as voice data 130.
Narrators are typically used to create recorded speech of high quality for different domains. The recorded speech is stored within a data store, such as voice data 130. Some of the voice data may be static prompts for the specific domains. For example, prompts for a specific domain such as a navigation system are stored as static phrases (e.g. turn left, turn left onto, arrive at, stay to the right, merge onto, and the like. Other voice data is also stored in voice data 130 and/or some other data store. The speech is segmented into one or more of: phonemes, diphones, syllables, morphemes, words, phrases and sentences. Generally, each sound in a chosen language is recorded in at least one voice such that the TTS engine can select the appropriate sounds to create the desired speech.
Domain detector 122 may determine the domain from an analysis of the input text. For example, if the input text has text phrases such as turn left, turn left onto, arrive at, stay to the right, merge onto, and the like then the domain may be classified as a turn-by-turn navigation domain. According to an embodiment, domain detector 122 is configured to automatically determine the domain without using specific markup instructions within the input text 105. Domain detector 122 may also utilize one or more classification algorithms (e.g. an Adaboost (Classification) based algorithm) to assist in determining the domain.
Speech manager 120 uses the identified domain and unit detector 124 to determine if there are domain specific speech recordings (e.g. pre-recorded static phrases) directly from input text 105. Speech recordings are obtained from voice data 130 based on the static phrases for the domain that are identified from input text 105. Unit detector 124 classifies the static parts (or prompts) in the specific domains as non-uniform units as identified from input text 105.
For example, assume a portion of the text input contains the following: Turn left onto Villa street; Bear right onto North road; Depart Gore Orphanage Road; Arrive at Manning Avenue; Drive 1.2 miles; . . . Unit detector 124 and identifies the static parts as non-uniform units (bolded portions are classified as non-uniform units):: Turn left onto Villa street; Bear right onto North road; Depart Gore Orphanage Road; Arrive at Manning Avenue; Drive 1.2 miles; . . . Unit detector 124 locates the recordings of the static phrases within voice data 130. As can be seen in the current example, the prompts for the domain in this example are classified as non-uniform units. According to an embodiment, not all of the prompts are classified as non-uniform units. For example, some units with a single short word (usually, functional words like “in”, “at”, “on”) may not be defined as non-uniform units. Further, for some units with long phrases where some breaks are located within the phrase (e.g. “take the exit # and follow signs for”, “at the end of the ramp # bear right on”) may be split into several parts based on the breaks, and then each of the split parts may be defined as a non-uniform unit.
During runtime of the speech application, unit detector 124 detects the non-uniform units from the input text. Generally, the word and phoneme sequence of non-uniform units are matched. According to an embodiment, context constraints are not used during an initial matching that may result in some errors for the non-uniform units detection. For example, the non-uniform unit “drive” may be located in several different positions (beginning, ending) of phrase. According to an embodiment, the first word at the beginning of phrase is classified as non-uniform units. For example, if the phrase is drive two miles, then drive is classified as a non-uniform unit.
Some detected non-uniform units may overlap. For example, the non-uniform units “turn left” and “turn left onto” overlaps. When the input text has the word sequence of “turn left onto”, both non-uniform units (turn left” and “turn left onto”) match with the input text. In the current example, “turn left” is a mismatch. Some identified static phrases may cross overlap (i.e. ABCD->“ABC”+“D” or “A”+“BCD”). In the cross overlap case, context constraints may be used to distinguish them. The following is an example of context constraints that may be used: Unit position in the phrase: begin, middle, end or single; Left/right word type: such as digit, acronym, . . . A maximum matching length may also be used to help during the non-uniform matching. For example, the non-uniform units are matched using the maximum matching length.
Blending unit 126 is configured to blend the static parts of the input text with the dynamic parts of the input text. Blending unit 126 uses the prosody contour (pitch/duration) for the non-uniform units. The prosody contour is stable for a domain since there is limited variation in narration of the static parts. The prosody contour from the non-uniform units is used to refine the target prosody trajectory. The refined target trajectory is used to calculate the target cost.
The following is an example of a target trajectory refinement process that refines the pitch contour. (1) For each basic unit related to the non-uniform unit, the candidate F0 mean is calculated (denote as mi cand, i=1, . . . , N) from the F0 values of all non-uniform candidates. (2) Calculate the target F0 mean (denote as mi tgt, i=1, . . . , N) for each unit based on the target pitch contour (Ot tgt, t=1, . . . , T). (3) Based on the distance between mi cand and mi tgt windowed weight functions are used to add the weighted distance to the target pitch contour to obtain the refined pitch contour.
The TTS engine blends the static phrases with the TTS output to smooth the acoustic trajectory of the input text. The prosody of the static phrases is used to create similar prosody in the TTS output.
FIG. 2 shows splitting a non-uniform unit into different parts. As illustrated, FIG. 2 includes uniform unit 205, non-uniform unit 210, and uniform unit 220.
During unit pre-selection, unit candidates are pre-selected for non-uniform unit 210. Non-uniform unit 210 is split into different parts comprising transition part 212, central part 214 and transition part 216. Transition parts 212 and 216 are located near the boundary between the non-uniform units 210 and uniform units 205 and 220. The length of transition part is configurable. For example, the length may be set based on a size of the non-uniform unit, a pre-set size, a percentage of size of the non-uniform unit and the like. Other information may also be used to determine the transition length (e.g. use break information). The central part of non-uniform unit 210 is the part of non-uniform unit excluding the part of the non-uniform unit used for transition between the uniform units and the non-uniform unit.
FIG. 3 shows blending in statistical parametric text-to-speech.
As illustrated, FIG. 3 comprises static part 310, transition part 320, and dynamic part 330.
For each non-uniform unit in the static part 310, the acoustic trajectory from the recording is extracted. A mean 312 is calculated for the static part 310 and a mean 332 for the dynamic part 330. Dynamic part 330 includes a variance 334. During runtime of the application using speech, the acoustic trajectory is used for synthesis instead of prediction from the statistical models after the non-uniform unit detection. For transition part 320, the variance 314 is enlarged before sending to prediction module. For the static part 310 not in transition part, the variance is set to 0, but for transition part 320, a small variance 314 is used to smooth the concatenation between the static part 310 and the dynamic part 300.
FIG. 4 shows an illustrative process for blending recorded speech with text-to-speech for specific domains. When reading the discussion of the routines presented herein, it should be appreciated that the logical operations of various embodiments are implemented (1) as a sequence of computer implemented acts or program modules running on a computing system and/or (2) as interconnected machine logic circuits or circuit modules within the computing system. The implementation is a matter of choice dependent on the performance requirements of the computing system implementing the invention. Accordingly, the logical operations illustrated and making up the embodiments described herein are referred to variously as operations, structural devices, acts or modules. These operations, structural devices, acts and modules may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof.
After a start operation, process 400 flows to operation 410, where the input text is received. For example, input text for a turn-by-turn navigation domain may be received. Generally, the input text is associated with a specific domain that includes pre-recorded static prompts for the speech application (e.g. navigation prompts, help prompts, dialing prompts . . . ).
Moving to operation 420, the domain that is associated with the input text is identified. According to an embodiment, the domain is automatically determined from an analysis of the input text without using specific markup instructions to identify the domain. For example, the input text may be searched for prompts that are matched with a specific domain. One or more classification algorithms (e.g. an Adaboost (Classification) based algorithm) may also be used assist in determining the domain.
Flowing to operation 430, the units are detected. The input text is analyzed to identify the non-uniform units in the input text that are static part (440) such as prompts used for the domain. The portion of the input text that is not pre-recorded and is to be synthesized is determined (450).
Flowing to operation 460, the static part and dynamic parts are blended together. Generally, the prosody contour (pitch/duration) for the non-uniform units of the static prompts are used to refine the target trajectory of the synthesized speech.
FIG. 5 illustrates an exemplary system for blending recorded speech with TTS. As illustrated, system 500 includes service 210, data store 245, touch screen input device/display 250 (e.g. a slate) and smart phone 230.
As illustrated, service 210 is a cloud based and/or enterprise based service that may be configured to provide services, such as productivity services (e.g. MICROSOFT OFFICE 365 or some other cloud based/online service that is used to interact with items (e.g. messages, spreadsheets, documents, charts, and the like). The service may be interacted with using different types of input/output. For example, a user may use touch input, hardware based input, speech input, and the like. The service may provide speech output that combines pre-recorded speech and synthesized speech. Functionality of one or more of the services/applications provided by service 210 may also be configured as a client/server based application. For example, a client device may include an application that performs operations that utilize recorded speech that is blended with TTS. Although system 500 shows a service relating to productivity applications, other services/applications may be configured.
System 500 as illustrated comprises a touch screen input device/display 250 (e.g. a slate/tablet device) and smart phone 230 that detects when a touch input has been received (e.g. a finger touching or nearly touching the touch screen). Any type of touch screen may be utilized that detects a user's touch input. For example, the touch screen may include one or more layers of capacitive material that detects the touch input. Other sensors may be used in addition to or in place of the capacitive material. For example, Infrared (IR) sensors may be used. According to an embodiment, the touch screen is configured to detect objects that in contact with or above a touchable surface. Although the term “above” is used in this description, it should be understood that the orientation of the touch panel system is irrelevant. The term “above” is intended to be applicable to all such orientations. The touch screen may be configured to determine locations of where touch input is received (e.g. a starting point, intermediate points and an ending point). Actual contact between the touchable surface and the object may be detected by any suitable means, including, for example, by a vibration sensor or microphone coupled to the touch panel. A non-exhaustive list of examples for sensors to detect contact includes pressure-based mechanisms, micro-machined accelerometers, piezoelectric devices, capacitive sensors, resistive sensors, inductive sensors, laser vibrometers, and LED vibrometers.
According to an embodiment, smart phone 230 and touch screen input device/display 250 are configured to receive text/speech input and output text/speech. Smart phone 230 and touch screen input device/display 250 may also be configured to include speech applications (e.g. applications 235 and 255).
As illustrated, touch screen input device/display 250 and smart phone 230 shows exemplary displays 252/232 showing the use of an application using speech (235, 255). For example, a user associated with slate device 250 may be using application 255 to find a restaurant. A user associated with smartphone 230 may be interacting with application 235 that provides navigation services. Many other types of applications may utilize speech. Data may be stored on a device (e.g. smart phone 230, slate 250 and/or at some other location (e.g. network data store 245). The applications 235, 255 may be a client based application, a server based application, a cloud based application and/or some combination.
Speech manager 242 is configured to perform operations relating to speech applications. While manager 242 is shown within service 210, the functionality of the manager may be included in other locations (e.g. on smart phone 230 and/or slate device 250).
FIG. 6 is a block diagram illustrating example physical components of a computing device 900 with which embodiments of the invention may be practiced. The computing device components described below may be suitable for the computing devices described above. In a basic configuration, computing device 900 may include at least one processing unit 702 and a system memory 704. Depending on the configuration and type of computing device, system memory 704 may comprise, but is not limited to, volatile (e.g. random access memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory 704 may include operating system 705, one or more programming modules 706, and may include a web browser application 720. Operating system 705, for example, may be suitable for controlling computing device 900's operation. In one embodiment, programming modules 706 may include a speech manager 242, as described above, installed on computing device 900. Furthermore, embodiments of the invention may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 6 by those components within a dashed line 708.
Computing device 900 may have additional features or functionality. For example, computing device 900 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 6 by a removable storage 709 and a non-removable storage 710.
As stated above, a number of program modules and data files may be stored in system memory 704, including operating system 705. While executing on processing unit 702, programming modules 706, such as the manager may perform processes including, for example, method 400 as described above. The aforementioned process is an example, and processing unit 702 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present invention may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, etc.
Furthermore, embodiments of the invention may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. For example, embodiments of the invention may be practiced via a system-on-a-chip (SOC) where each or many of the components illustrated in FIG. 6 may be integrated onto a single integrated circuit. Such an SOC device may include one or more processing units, graphics units, communications units, system virtualization units and various application functionality all of which are integrated (or “burned”) onto the chip substrate as a single integrated circuit. When operating via an SOC, the functionality, described herein, with respect to the manager 242 may be operated via application-specific logic integrated with other components of the computing device/system 900 on the single integrated circuit (chip). Embodiments of the invention may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the invention may be practiced within a general purpose computer or in any other circuits or systems.
FIGS. 7A and 7B illustrate a suitable mobile computing environment, for example, a mobile telephone, a smartphone, a tablet personal computer, a laptop computer, and the like, with which embodiments of the invention may be practiced. With reference to FIG. 7A, an example mobile computing device 800 for implementing the embodiments is illustrated. In a basic configuration, mobile computing device 800 is a handheld computer having both input elements and output elements. Input elements may include touch screen display 805 and input buttons 815 that allow the user to enter information into mobile computing device 800. Mobile computing device 800 may also incorporate an optional side input element 815 allowing further user input. Optional side input element 815 may be a rotary switch, a button, or any other type of manual input element. In alternative embodiments, mobile computing device 800 may incorporate more or less input elements. For example, display 805 may not be a touch screen in some embodiments. In yet another alternative embodiment, the mobile computing device is a portable phone system, such as a cellular phone having display 805 and input buttons 815. Mobile computing device 800 may also include an optional keypad 835. Optional keypad 815 may be a physical keypad or a “soft” keypad generated on the touch screen display.
FIG. 7B is a block diagram illustrating components of a mobile computing device used in one embodiment, such as the computing device shown in FIG. 7A. That is, mobile computing device 800 can incorporate system 802 to implement some embodiments. For example, system 802 can be used in implementing a “smart phone” that can run one or more applications similar to those of a desktop or notebook computer such as, for example, browser, e-mail, scheduling, instant messaging, and media player applications. In some embodiments, system 802 is integrated as a computing device, such as an integrated personal digital assistant (PDA) and wireless phoneme.
One or more application programs 866 may be loaded into memory 862 and run on or in association with operating system 864. Examples of application programs include phoneme dialer programs, e-mail programs, PIM (personal information management) programs, word processing programs, spreadsheet programs, Internet browser programs, messaging programs, and so forth. System 802 also includes non-volatile storage 868 within memory 862. Non-volatile storage 868 may be used to store persistent information that should not be lost if system 802 is powered down. Applications 866 may use and store information in non-volatile storage 868, such as e-mail or other messages used by an e-mail application, and the like. A synchronization application (not shown) may also reside on system 802 and is programmed to interact with a corresponding synchronization application resident on a host computer to keep the information stored in non-volatile storage 868 synchronized with corresponding information stored at the host computer. As should be appreciated, other applications may be loaded into memory 862 and run on the device 800, including the speech manager 242, described above.
FIG. 7 illustrates a system architecture for blending recorded speech with TTS output, as described above.
Components managed via the speech manager 242 may be stored in different communication channels or other storage types. For example, components along with information from which they are developed may be stored using directory services 1022, web portals 1024, mailbox services 1026, instant messaging stores 1028 and social networking sites 1030. The systems/applications 242, 1020 may use any of these types of systems or the like for enabling management and storage of components in a store 1016. A server 1032 may provide communications for managed components and content to clients. As one example, server 1032 may provide speech related services. Server 1032 may provide services and content over the web to clients through a network 1008. Examples of clients that may utilize server 1032 include computing device 1002, which may include any general purpose personal computer, a tablet computing device 1004 and/or mobile computing device 1006 which may include smart phones. Any of these devices may obtain display component management communications and content from the store 1016.
1. A method for blending recorded speech with text-to-speech (TTS) for specific domains, comprising:
identifying a domain from the input text;
determining a static part from the input text that has previously been recorded and stored within a data store, wherein determining the static part comprises detecting the static part based on recorded units for the identified domain;
determining a dynamic part from the input text; and
blending the static part with the dynamic part within a TTS engine.
2. The method of claim 1, wherein blending the static part with the dynamic part within the TTS engine comprises smoothing an acoustic trajectory of a transition between the static part and the dynamic part based on the recorded units for the static part and a predicted trajectory.
3. The method of claim 1, further comprising creating a transition at a boundary of the static part and the dynamic part.
4. The method of claim 1, further comprising obtaining a speech output from a text to speech (TTS) synthesizer.
5. The method of claim 1, further comprising attempting to maintain a prosody of the static part in the dynamic part output by a TTS synthesizer.
6. The method of claim 1, further comprising splitting a portion of identified non-uniform units from the input text into a transition part and a central part.
7. The method of claim 6, wherein the central part of the identified non-uniform units excludes a part of the identified non-uniform units used for transition between uniform parts and the identified non-uniform units.
8. A computer storage device having computer-executable instructions for blending recorded speech with text-to-speech (TTS) for specific domains, comprising:
identifying a domain from the input text that identifies a type of speech application;
9. The computer storage device of claim 8, wherein blending the static part with the dynamic part within the TTS engine comprises smoothing an acoustic trajectory of a transition between the static part and the dynamic part based on recorded units for the static part and a predicted trajectory.
10. The computer storage device of claim 8, further comprising creating a transition at a boundary of the static part and the dynamic part.
11. The computer storage device of claim 8, further comprising attempting to maintain a prosody of the static part in the dynamic part output by a TTS synthesizer.
12. The computer storage device of claim 8, further comprising splitting a portion of identified non-uniform units from the input text into a transition part and a central part and adjusting the transition part to smooth a transition between uniform units.
13. A system for blending recorded speech with text-to-speech (TTS) for specific domains, comprising:
locating recorded speech for the static part from the data store;
blending the recorded speech with the static part with the dynamic part within a TTS engine.
14. The system of claim 13, wherein blending the static part with the dynamic part within the TTS engine comprises smoothing an acoustic trajectory of a transition between the static part and the dynamic part based on recorded units for the static part and a predicted trajectory.
15. The system of claim 13, further comprising creating a transition at a boundary of the static part and the dynamic part.
16. The system of claim 13, further comprising attempting to maintain a prosody of the static part in the dynamic part output by a TTS synthesizer and splitting a portion of identified non-uniform units from the input text into a transition part and a central part and adjusting the transition part to smooth a transition between uniform units.
17. The method of claim 8, further comprising adjusting the transition part to smooth a transition between uniform units.
18. The method of claim 8, wherein the transition part is located near a boundary between the non-uniform units and uniform units.
19. The computer storage device of claim 12, wherein the transition part is located near a boundary between the non-uniform units and the uniform units.
20. The system of claim 16, wherein the transition part is located near a boundary between the non-uniform units and the uniform units.
US13/547,459 2012-07-12 2012-07-12 Blending recorded speech with text-to-speech output for specific domains Active 2033-06-07 US8996377B2 (en)
US13/547,459 US8996377B2 (en) 2012-07-12 2012-07-12 Blending recorded speech with text-to-speech output for specific domains
US20140019134A1 US20140019134A1 (en) 2014-01-16
US8996377B2 true US8996377B2 (en) 2015-03-31
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US13/547,459 Active 2033-06-07 US8996377B2 (en) 2012-07-12 2012-07-12 Blending recorded speech with text-to-speech output for specific domains
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Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ZHAO, SHENG;WANG, PENG;GAO, DIFEI;AND OTHERS;SIGNING DATES FROM 20120704 TO 20120705;REEL/FRAME:028716/0907