Patent Publication Number: US-8996366-B2

Title: Multi-stage speaker adaptation

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 14/035,499, filed Sep. 24, 2013, and issued as U.S. Pat. No. 8,700,393. U.S. patent application Ser. No. 14/035,499 is a continuation of U.S. patent application Ser. No. 13/653,792, filed Oct. 17, 2012, and issued as U.S. Pat. No. 8,571,859. U.S. patent application Ser. No. 13/653,792 claims priority to provisional U.S. patent application No. 61/653,680, filed May 31, 2012. All of these patents, applications, and provisional applications are hereby incorporated by reference in their entirety. 
    
    
     BACKGROUND 
     Automatic speech recognition (ASR) technology can be used to map audio utterances to textual representations of those utterances. In some systems, ASR involves comparing characteristics of the audio utterances to an acoustic model of human voice. However, different speakers may exhibit different speech characteristics (e.g., pitch, accent, tempo, etc.). Consequently, the acoustic model may not perform well for all speakers. 
     SUMMARY 
     In a first example embodiment, a first gender-specific speaker adaptation technique may be selected based on characteristics of a first set of feature vectors. The first set of feature vectors may correspond to a first unit of input speech, and may be configured for use in automatic speech recognition (ASR) of the first unit of input speech. A second set of feature vectors may be modified based on the first gender-specific speaker adaptation technique. The second set of feature vectors may correspond to a second unit of input speech. The modified second set of feature vectors may be configured for use in ASR of the second unit of input speech. A first speaker-dependent speaker adaptation technique may be selected based on characteristics of the second set of feature vectors. A third set of feature vectors may be modified based on the first speaker-dependent speaker adaptation technique. The third set of feature vectors may correspond to a third unit of input speech. The modified third set of feature vectors may be configured for use in ASR of the third unit of input speech. 
     In a second example embodiment, a first set of feature vectors may be obtained. The first set of feature vectors may correspond to a first unit of input speech. Characteristics of the first set of feature vectors may be compared to a first gender-specific speech model and a second gender-specific speech model. The characteristics of the first set of feature vectors may be determined to fit the first gender-specific speech model better than the second gender-specific model. A second set of feature vectors may be obtained. The second set of feature vectors may correspond to a second unit of input speech. The second set of feature vectors may be modified based on a first gender-specific speaker adaptation technique associated with the first gender-specific speech model. After modifying the second set of feature vectors, characteristics of the second set of feature vectors may be compared to the first gender-specific speech model, the second gender-specific speech model, and a speaker-dependent speech model. The characteristics of the second set of feature vectors may be determined to fit the speaker-dependent speech model better than the first and second gender-specific models. A third set of feature vectors may be obtained. The third set of feature vectors may correspond to a third unit of input speech. The third set of feature vectors may be modified based on a speaker-dependent speaker adaptation technique associated with the speaker-dependent speech model. 
     A third example embodiment may include a non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by a computing device, cause the computing device to perform operations in accordance with the first and/or second example embodiments. 
     A fourth example embodiment may include a computing device, comprising at least a processor and data storage. The data storage may contain program instructions that, upon execution by the processor, operate in accordance with the first and/or second example embodiments. 
     These as well as other aspects, advantages, and alternatives will become apparent to those of ordinary skill in the art by reading the following detailed description with reference where appropriate to the accompanying drawings. Further, it should be understood that the description provided in this summary section and elsewhere in this document is intended to illustrate the claimed subject matter by way of example and not by way of limitation. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1  depicts a distributed computing architecture, in accordance with an example embodiment. 
         FIG. 2A  is a block diagram of a server device, in accordance with an example embodiment. 
         FIG. 2B  depicts a cloud-based server system, in accordance with an example embodiment. 
         FIG. 3  depicts a block diagram of a client device, in accordance with an example embodiment. 
         FIG. 4  depicts an ASR system, in accordance with an example embodiment. 
         FIG. 5  depicts aspects of an acoustic model, in accordance with an example embodiment. 
         FIG. 6  depicts an ASR system search graph, in accordance with an example embodiment. 
         FIG. 7  depicts an ASR system that supports speaker adaptation, in accordance with an example embodiment. 
         FIG. 8A  is a message flow diagram of speaker adaptation, in accordance with an example embodiment. 
         FIG. 8B  is another message flow diagram of speaker adaptation, in accordance with an example embodiment. 
         FIG. 9  is a flow chart, in accordance with an example embodiment. 
         FIGS. 10A and 10B  are another flow chart, in accordance with an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     1. Overview 
     ASR systems may include an acoustic model. The acoustic model may be speaker independent, in that it can represent the temporal and/or spectral characteristics of various sub-word sounds (e.g., phonemes) of a hypothetical “average” speaker. However, utterances from different speakers can have different qualities (e.g., different frequencies, tempos, accents, etc.). Thus, an acoustic model may perform reasonably well for most speakers, but may perform poorly when processing the utterances of some speakers—particularly, speakers whose voices exhibit temporal and/or spectral characteristics that have not been appropriately represented in the acoustic model. 
     Speaker-dependent acoustic models can also be developed. These acoustic models are tuned to the speech characteristics of a particular speaker. However, developing a speaker-dependent acoustic model may involve collecting a great deal of accurately-transcribed utterances of this particular speaker. Thus, despite the error rates of speaker-dependent acoustic models being lower than those of speaker-independent acoustic models, speaker-dependent acoustic models may not always be possible or practical to develop. 
     Speaker adaptation can also be used with acoustic models. At a high level, speaker adaptation may involve the ASR system determining (i) the temporal and/or spectral characteristics of a particular speaker&#39;s voice, (ii) the difference between these characteristics and associated characteristics of the acoustic model, and (iii) developing a transform that maps the temporal and/or spectral characteristics of the particular speaker&#39;s voice to a representation that is closer to that of the acoustic model. This transform may then be applied to subsequent utterances received from the speaker, and the acoustic model may be applied to the result. Developing the transform and applying it to map the speaker&#39;s vocal characteristics to an acoustic model can be referred to as feature-space speaker adaptation. 
     Additionally or alternatively, speaker adaptation may involve adjusting the acoustic model itself based on the characteristics of the particular speaker&#39;s voice. For instance, once the ASR system has processed a sufficiently large set of samples of the speaker&#39;s utterances using an initial acoustic model, a new acoustic model can be derived from these utterances. The new acoustic model may be used in place of the initial acoustic model. This approach can be referred to as model-space speaker adaptation. Typically, more samples of the speaker&#39;s utterances and more computing resources are used to perform model-space speaker adaptation than are used to perform feature-space speaker adaptation. 
     Given that speaker adaptation can be beneficial, improving the performance of speaker adaptation is desirable. Particularly, among other features, the embodiments herein disclose various aspects of multi-stage speaker adaptation. 
     An ASR system may receive a significant amount of speech input from a particular speaker (perhaps several seconds or more) before it can apply a speaker-dependent speaker adaptation technique. Herein, a speaker adaptation technique may refer to any mechanism for adapting input speech, and a specific speaker adaptation technique may be associated with one or more speaker adaptation profiles. These profiles, in turn, may define or be associated with parameters that are used for speaker adaptation. 
     In the time before the ASR system applies the speaker-dependent speaker adaptation technique, the ASR system may be processing the particular speaker&#39;s speech input without speaker adaptation. Therefore, during this period, the ASR system&#39;s speech recognition performance may be limited to that provided by a speaker-independent acoustic model. 
     However, it may be possible for the ASR system to apply an intermediate degree of speaker adaptation before performing speaker-dependent speaker adaptation. For instance, male and female voices tend to have distinct differences, such as different frequency ranges. Thus, rapidly identifying the gender of a speaker may be possible based on the spectral characteristics of a relatively small amount of speech. Once the speaker&#39;s gender is identified, the ASR system may apply a gender-specific speaker adaptation technique to input utterances until the speaker has been identified. Then, a speaker-dependent speaker adaptation technique for the identified speaker may be applied. In this way, the speech recognition accuracy of the ASR system may be improved, to some extent, before the speaker is identified. 
     Further, the ASR system may continue performing aspects of speaker adaptation after the speaker is identified and speaker-dependent speaker adaptation is being performed. For instance, the ASR system may have access to multiple speaker adaptation profiles for some speakers. Each profile may be associated with the speaker speaking in a particular environment or location. One possible environment-specific, speaker-dependent speaker adaptation profile might be based on the speaker speaking in a quiet location, with little background noise. Another possible environment-specific, speaker-dependent speaker adaptation profile might be based on the speaker speaking in an environment with a particular type of background noise, such as an office or a car. The ASR system may choose an environment-specific, speaker-dependent speaker adaptation profile for the speaker based on the characteristics of the input utterances (e.g., some combination of the speaker&#39;s voice and the background noise) and/or the speaker&#39;s current location. 
     In some environments, devices involved in ASR may be shared by two or more speakers. For example, two individuals may be using the ASR feature of a tablet computer for transcription purposes, and may be passing the tablet computer back and forth to one another as they take turns speaking into it. Or, several individuals may be conducting a video conference in which ASR occurs either to provide real-time transcriptions of utterances, and/or to provide a transcribed record of the conversation. 
     In these situations, the ASR system may change speaker-adaptation techniques in various ways. For instance the ASR system may begin with no speaker adaptation, and then determine that a male speaker is speaking. Possibly in response to making this determination, the ASR system may begin applying a male-specific speaker adaptation technique to the input speech. Perhaps a few second later, the ASR system may have gathered a sufficient amount of information from this speaker&#39;s utterances to identify the speaker. Consequently, the ASR system may begin applying a speaker-dependent speaker adaptation technique to the input speech. 
     Later, the ASR system may determine that a female speaker is speaking, and begin applying a female-specific speaker adaptation technique to the input speech. Once the ASR system has gathered a sufficient amount of information from this speaker&#39;s utterances to identify the speaker, the ASR system may begin applying a speaker-dependent speaker adaptation technique to the input speech. Other embodiments are possible as well. 
     ASR systems can be deployed in various environments. Some ASR systems are employed in a user device (e.g., a personal computer, tablet computer, or wireless communication device). A user speaks utterances into the device, and the ASR system in the device transcribes the utterances into one or more text strings. Other ASR systems are server-based. A user speaks an utterance into a client device, and the client device transmits the utterance (e.g., in an encoded form) to a server device. Then, the server device performs ASR on the utterance and transmits one or more text string mappings to the client device. Nonetheless, aspects of ASR systems may be distributed in various ways between client and server devices. 
     The above processes, and example embodiments thereof, will be described in detail in Sections 5 and 6. However, in order to further embody ASR system implementations, the next three sections describe, respectively, example computing systems and devices that may support ASR systems, an overview of ASR system components and functions, and an overview of ASR system operation. 
     2. Example Communication System and Device Architecture for Supporting Automatic Speech Recognition 
     The methods, devices, and systems described herein can be implemented using client devices and/or so-called “cloud-based” server devices. Under various aspects of this paradigm, client devices, such as mobile phones, tablet computers, and/or desktop computers, may offload some processing and storage responsibilities to remote server devices. At least some of the time, these client services are able to communicate, via a network such as the Internet, with the server devices. As a result, applications that operate on the client devices may also have a persistent, server-based component. Nonetheless, it should be noted that at least some of the methods, processes, and techniques disclosed herein may be able to operate entirely on a client device or a server device. 
     Furthermore, the “server devices” described herein may not necessarily be associated with a client/server architecture, and therefore may also be referred to as “computing devices.” Similarly, the “client devices” described herein also may not necessarily be associated with a client/server architecture, and therefore may be interchangeably referred to as “user devices.” In some contexts, “client devices” may also be referred to as “computing devices.” 
     This section describes general system and device architectures for such client devices and server devices. However, the methods, devices, and systems presented in the subsequent sections may operate under different paradigms as well. Thus, the embodiments of this section are merely examples of how these methods, devices, and systems can be enabled. 
     A. Communication System 
       FIG. 1  is a simplified block diagram of a communication system  100 , in which various embodiments described herein can be employed. Communication system  100  includes client devices  102 ,  104 , and  106 , which represent a desktop personal computer (PC), a tablet computer, and a mobile phone, respectively. Each of these client devices may be able to communicate with other devices via a network  108  through the use of wireline connections (designated by solid lines) and/or wireless connections (designated by dashed lines). 
     Network  108  may be, for example, the Internet, or some other form of public or private Internet Protocol (IP) network. Thus, client devices  102 ,  104 , and  106  may communicate using packet-switching technologies. Nonetheless, network  108  may also incorporate at least some circuit-switching technologies, and client devices  102 ,  104 , and  106  may communicate via circuit switching alternatively or in addition to packet switching. 
     A server device  110  may also communicate via network  108 . Particularly, server device  110  may communicate with client devices  102 ,  104 , and  106  according to one or more network protocols and/or application-level protocols to facilitate the use of network-based or cloud-based computing on these client devices. Server device  110  may include integrated data storage (e.g., memory, disk drives, etc.) and may also be able to access a separate server data storage  112 . Communication between server device  110  and server data storage  112  may be direct, via network  108 , or both direct and via network  108  as illustrated in  FIG. 1 . Server data storage  112  may store application data that is used to facilitate the operations of applications performed by client devices  102 ,  104 , and  106  and server device  110 . 
     Although only three client devices, one server device, and one server data storage are shown in  FIG. 1 , communication system  100  may include any number of each of these components. For instance, communication system  100  may comprise millions of client devices, thousands of server devices and/or thousands of server data storages. Furthermore, client devices may take on forms other than those in  FIG. 1 . 
     B. Server Device 
       FIG. 2A  is a block diagram of a server device in accordance with an example embodiment. In particular, server device  200  shown in  FIG. 2A  can be configured to perform one or more functions of server device  110  and/or server data storage  112 . Server device  200  may include a user interface  202 , a communication interface  204 , processor  206 , and data storage  208 , all of which may be linked together via a system bus, network, or other connection mechanism  214 . 
     User interface  202  may comprise user input devices such as a keyboard, a keypad, a touch screen, a computer mouse, a trackball, a joystick, and/or other similar devices, now known or later developed. User interface  202  may also comprise user display devices, such as one or more cathode ray tubes (CRT), liquid crystal displays (LCD), light emitting diodes (LEDs), displays using digital light processing (DLP) technology, printers, light bulbs, and/or other similar devices, now known or later developed. Additionally, user interface  202  may be configured to generate audible output(s), via a speaker, speaker jack, audio output port, audio output device, earphones, and/or other similar devices, now known or later developed. In some embodiments, user interface  202  may include software, circuitry, or another form of logic that can transmit data to and/or receive data from external user input/output devices. 
     Communication interface  204  may include one or more wireless interfaces and/or wireline interfaces that are configurable to communicate via a network, such as network  108  shown in  FIG. 1 . The wireless interfaces, if present, may include one or more wireless transceivers, such as a BLUETOOTH® transceiver, a Wifi transceiver perhaps operating in accordance with an IEEE 802.11 standard (e.g., 802.11b, 802.11g, 802.11n), a WiMAX transceiver perhaps operating in accordance with an IEEE 802.16 standard, a Long-Term Evolution (LTE) transceiver perhaps operating in accordance with a 3rd Generation Partnership Project (3GPP) standard, and/or other types of wireless transceivers configurable to communicate via local-area or wide-area wireless networks. The wireline interfaces, if present, may include one or more wireline transceivers, such as an Ethernet transceiver, a Universal Serial Bus (USB) transceiver, or similar transceiver configurable to communicate via a twisted pair wire, a coaxial cable, a fiber-optic link or other physical connection to a wireline device or network. 
     Processor  206  may include one or more general purpose processors (e.g., microprocessors) and/or one or more special purpose processors (e.g., digital signal processors (DSPs), graphical processing units (GPUs), floating point processing units (FPUs), network processors, or application specific integrated circuits (ASICs)). Processor  206  may be configured to execute computer-readable program instructions  210  that are contained in data storage  208 , and/or other instructions, to carry out various functions described herein. 
     Thus, data storage  208  may include one or more non-transitory computer-readable storage media that can be read or accessed by processor  206 . The one or more computer-readable storage media may include volatile and/or non-volatile storage components, such as optical, magnetic, organic or other memory or disc storage, which can be integrated in whole or in part with processor  206 . In some embodiments, data storage  208  may be implemented using a single physical device (e.g., one optical, magnetic, organic or other memory or disc storage unit), while in other embodiments, data storage  208  may be implemented using two or more physical devices. 
     Data storage  208  may also include program data  212  that can be used by processor  206  to carry out functions described herein. In some embodiments, data storage  208  may include, or have access to, additional data storage components or devices (e.g., cluster data storages described below). 
     C. Server Clusters 
     Server device  110  and server data storage device  112  may store applications and application data at one or more places accessible via network  108 . These places may be data centers containing numerous servers and storage devices. The exact physical location, connectivity, and configuration of server device  110  and server data storage device  112  may be unknown and/or unimportant to client devices. Accordingly, server device  110  and server data storage device  112  may be referred to as “cloud-based” devices that are housed at various remote locations. One possible advantage of such “cloud-based” computing is to offload processing and data storage from client devices, thereby simplifying the design and requirements of these client devices. 
     In some embodiments, server device  110  and server data storage device  112  may be a single computing device residing in a single data center. In other embodiments, server device  110  and server data storage device  112  may include multiple computing devices in a data center, or even multiple computing devices in multiple data centers, where the data centers are located in diverse geographic locations. For example,  FIG. 1  depicts each of server device  110  and server data storage device  112  potentially residing in a different physical location. 
       FIG. 2B  depicts a cloud-based server cluster in accordance with an example embodiment. In  FIG. 2B , functions of server device  110  and server data storage device  112  may be distributed among three server clusters  220 A,  220 B, and  220 C. Server cluster  220 A may include one or more server devices  200 A, cluster data storage  222 A, and cluster routers  224 A connected by a local cluster network  226 A. Similarly, server cluster  220 B may include one or more server devices  200 B, cluster data storage  222 B, and cluster routers  224 B connected by a local cluster network  226 B. Likewise, server cluster  220 C may include one or more server devices  200 C, cluster data storage  222 C, and cluster routers  224 C connected by a local cluster network  226 C. Server clusters  220 A,  220 B, and  220 C may communicate with network  108  via communication links  228 A,  228 B, and  228 C, respectively. 
     In some embodiments, each of the server clusters  220 A,  220 B, and  220 C may have an equal number of server devices, an equal number of cluster data storages, and an equal number of cluster routers. In other embodiments, however, some or all of the server clusters  220 A,  220 B, and  220 C may have different numbers of server devices, different numbers of cluster data storages, and/or different numbers of cluster routers. The number of server devices, cluster data storages, and cluster routers in each server cluster may depend on the computing task(s) and/or applications assigned to each server cluster. 
     In the server cluster  220 A, for example, server devices  200 A can be configured to perform various computing tasks of server device  110 . In one embodiment, these computing tasks can be distributed among one or more of server devices  200 A. Server devices  200 B and  200 C in server clusters  220 B and  220 C may be configured the same or similarly to server devices  200 A in server cluster  220 A. On the other hand, in some embodiments, server devices  200 A,  200 B, and  200 C each may be configured to perform different functions. For example, server devices  200 A may be configured to perform one or more functions of server device  110 , and server devices  200 B and server device  200 C may be configured to perform functions of one or more other server devices. Similarly, the functions of server data storage device  112  can be dedicated to a single server cluster, or spread across multiple server clusters. 
     Cluster data storages  222 A,  222 B, and  222 C of the server clusters  220 A,  220 B, and  220 C, respectively, may be data storage arrays that include disk array controllers configured to manage read and write access to groups of hard disk drives. The disk array controllers, alone or in conjunction with their respective server devices, may also be configured to manage backup or redundant copies of the data stored in cluster data storages to protect against disk drive failures or other types of failures that prevent one or more server devices from accessing one or more cluster data storages. 
     Similar to the manner in which the functions of server device  110  and server data storage device  112  can be distributed across server clusters  220 A,  220 B, and  220 C, various active portions and/or backup/redundant portions of these components can be distributed across cluster data storages  222 A,  222 B, and  222 C. For example, some cluster data storages  222 A,  222 B, and  222 C may be configured to store backup versions of data stored in other cluster data storages  222 A,  222 B, and  222 C. 
     Cluster routers  224 A,  224 B, and  224 C in server clusters  220 A,  220 B, and  220 C, respectively, may include networking equipment configured to provide internal and external communications for the server clusters. For example, cluster routers  224 A in server cluster  220 A may include one or more packet-switching and/or routing devices configured to provide (i) network communications between server devices  200 A and cluster data storage  222 A via cluster network  226 A, and/or (ii) network communications between the server cluster  220 A and other devices via communication link  228 A to network  108 . Cluster routers  224 B and  224 C may include network equipment similar to cluster routers  224 A, and cluster routers  224 B and  224 C may perform networking functions for server clusters  220 B and  220 C that cluster routers  224 A perform for server cluster  220 A. 
     Additionally, the configuration of cluster routers  224 A,  224 B, and  224 C can be based at least in part on the data communication requirements of the server devices and cluster storage arrays, the data communications capabilities of the network equipment in the cluster routers  224 A,  224 B, and  224 C, the latency and throughput of the local cluster networks  226 A,  226 B,  226 C, the latency, throughput, and cost of the wide area network connections  228 A,  228 B, and  228 C, and/or other factors that may contribute to the cost, speed, fault-tolerance, resiliency, efficiency and/or other design goals of the system architecture. 
     D. Client Device 
       FIG. 3  is a simplified block diagram showing some of the components of an example client device  300 . By way of example and without limitation, client device  300  may be a “plain old telephone system” (POTS) telephone, a cellular mobile telephone, a still camera, a video camera, a fax machine, an answering machine, a computer (such as a desktop, notebook, or tablet computer), a personal digital assistant (PDA), a home automation component, a digital video recorder (DVR), a digital TV, a remote control, or some other type of device equipped with one or more wireless or wired communication interfaces. 
     As shown in  FIG. 3 , client device  300  may include a communication interface  302 , a user interface  304 , a processor  306 , and data storage  308 , all of which may be communicatively linked together by a system bus, network, or other connection mechanism  310 . 
     Communication interface  302  functions to allow client device  300  to communicate, using analog or digital modulation, with other devices, access networks, and/or transport networks. Thus, communication interface  302  may facilitate circuit-switched and/or packet-switched communication, such as POTS communication and/or IP or other packetized communication. For instance, communication interface  302  may include a chipset and antenna arranged for wireless communication with a radio access network or an access point. Also, communication interface  302  may take the form of a wireline interface, such as an Ethernet, Token Ring, or USB port. Communication interface  302  may also take the form of a wireless interface, such as a Wifi, BLUETOOTH®, global positioning system (GPS), or wide-area wireless interface (e.g., WiMAX or LTE). However, other forms of physical layer interfaces and other types of standard or proprietary communication protocols may be used over communication interface  302 . Furthermore, communication interface  302  may comprise multiple physical communication interfaces (e.g., a Wifi interface, a BLUETOOTH® interface, and a wide-area wireless interface). 
     User interface  304  may function to allow client device  300  to interact with a human or non-human user, such as to receive input from a user and to provide output to the user. Thus, user interface  304  may include input components such as a keypad, keyboard, touch-sensitive or presence-sensitive panel, computer mouse, trackball, joystick, microphone, still camera and/or video camera. User interface  304  may also include one or more output components such as a display screen (which, for example, may be combined with a presence-sensitive panel), CRT, LCD, LED, a display using DLP technology, printer, light bulb, and/or other similar devices, now known or later developed. User interface  304  may also be configured to generate audible output(s), via a speaker, speaker jack, audio output port, audio output device, earphones, and/or other similar devices, now known or later developed. In some embodiments, user interface  304  may include software, circuitry, or another form of logic that can transmit data to and/or receive data from external user input/output devices. Additionally or alternatively, client device  300  may support remote access from another device, via communication interface  302  or via another physical interface (not shown). 
     Processor  306  may comprise one or more general purpose processors (e.g., microprocessors) and/or one or more special purpose processors (e.g., DSPs, GPUs, FPUs, network processors, or ASICs). Data storage  308  may include one or more volatile and/or non-volatile storage components, such as magnetic, optical, flash, or organic storage, and may be integrated in whole or in part with processor  306 . Data storage  308  may include removable and/or non-removable components. 
     Processor  306  may be capable of executing program instructions  318  (e.g., compiled or non-compiled program logic and/or machine code) stored in data storage  308  to carry out the various functions described herein. Therefore, data storage  308  may include a non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by client device  300 , cause client device  300  to carry out any of the methods, processes, or functions disclosed in this specification and/or the accompanying drawings. The execution of program instructions  318  by processor  306  may result in processor  306  using data  312 . 
     By way of example, program instructions  318  may include an operating system  322  (e.g., an operating system kernel, device driver(s), and/or other modules) and one or more application programs  320  (e.g., address book, email, web browsing, social networking, and/or gaming applications) installed on client device  300 . Similarly, data  312  may include operating system data  316  and application data  314 . Operating system data  316  may be accessible primarily to operating system  322 , and application data  314  may be accessible primarily to one or more of application programs  320 . Application data  314  may be arranged in a file system that is visible to or hidden from a user of client device  300 . 
     Application programs  320  may communicate with operating system  322  through one or more application programming interfaces (APIs). These APIs may facilitate, for instance, application programs  320  reading and/or writing application data  314 , transmitting or receiving information via communication interface  302 , receiving or displaying information on user interface  304 , and so on. 
     In some vernaculars, application programs  320  may be referred to as “apps” for short. Additionally, application programs  320  may be downloadable to client device  300  through one or more online application stores or application markets. However, application programs can also be installed on client device  300  in other ways, such as via a web browser or through a physical interface (e.g., a USB port) on client device  300 . 
     3. Example Automatic Speech Recognition System Overview 
     Before describing speaker adaptation in detail, it may be beneficial to understand overall ASR system operation. Thus, this section describes ASR systems in general, including how the components of an ASR system may interact with one another in order to facilitate speech recognition, and how some of these components may be trained. 
       FIG. 4  depicts an example ASR system. At run-time, the input to the ASR system may include an utterance  400 , and the output may include one or more text strings and possibly associated confidence levels  414 . The components of the ASR system may include a feature analysis module  402  that produces feature vectors  404 , a pattern classification module  406 , an acoustic model  408 , a dictionary  410 , and a language model  412 . Pattern classification module  406  may incorporate various aspects of acoustic model  408 , dictionary  410 , and language model  412 . 
     It should be noted that the discussion in this section, and the accompanying figures, are presented for purposes of example. Other ASR system arrangements, including different components, different relationships between the components, and/or different processing, may be possible. 
     A. Feature Analysis Module 
     Feature analysis module  402  may receive utterance  400 . This utterance may include an analog or digital representation of human speech, and may possibly contain background noise as well. Feature analysis module  402  may convert utterance  400  to a sequence of one or more feature vectors  404 . Each of feature vectors  404  may include temporal and/or spectral representations of the acoustic features of at least a portion of utterance  400 . For instance, a feature vector may include mel-frequency cepstrum coefficients of such a portion. 
     The mel-frequency cepstrum coefficients may represent the short-term power spectrum of a portion of utterance  400 . They may be based on, for example, a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. (A mel scale may be a scale of pitches subjectively perceived by listeners to be about equally distant from one another, even though the actual frequencies of these pitches are not equally distant from one another.) 
     To derive these coefficients, feature analysis module  402  may sample and quantize utterance  400 , divide it into overlapping or non-overlapping frames of s milliseconds, and perform spectral analysis on the frames to derive the spectral components of each frame. Feature analysis module  402  may further perform noise removal and convert the standard spectral coefficients to mel-frequency cepstrum coefficients, and then calculate first-order and second-order cepstral derivatives of the mel-frequency cepstrum coefficients. 
     The first-order cepstral coefficient derivatives may be calculated based on the slopes of linear regressions performed over windows of two or more consecutive frames. The second-order cepstral coefficient derivatives may be calculated based on the slopes of linear regressions performed over windows of two or more consecutive sets of first-order cepstral coefficient derivatives. However, there may be other ways of calculating the first-order and second-order cepstral coefficient derivatives. 
     In some embodiments, one or more frames of utterance  400  may be represented by a feature vector of mel-frequency cepstrum coefficients, first-order cepstral coefficient derivatives, and second-order cepstral coefficient derivatives. For example, the feature vector may contain 13 coefficients, 13 first-order derivatives, and 13 second-order derivatives, therefore having a length of 39. However, feature vectors may use different combinations of features in other possible embodiments. 
     B. Pattern Classification Module 
     Pattern classification module  406  may receive a sequence of feature vectors  404  from feature analysis module  402  and produce, as output, one or more text string transcriptions  414  of utterance  400 . Each transcription  414  may be accompanied by a respective confidence level indicating an estimated likelihood that the transcription is correct (e.g., 80% confidence, 90% confidence, etc.). 
     To produce this output, pattern classification module  406  may include, or incorporate aspects of acoustic model  408 , dictionary  410 , and/or language model  412 . In some embodiments, pattern classification module  406  may also use a search graph that represents sequences of word or sub-word acoustic features that appear in spoken utterances. The behavior of pattern classification module  406  will be described below in the context of these modules. 
     C. Acoustic Model 
     Acoustic model  408  may determine probabilities that a particular sequence of feature vectors  404  were derived from a particular sequence of spoken words and/or sub-word sounds. This may involve mapping sequences of feature vectors to one or more phonemes, and then mapping sequences of phonemes to one or more words. 
     A phoneme may be considered to be the smallest segment of an utterance that encompasses a meaningful contrast with other segments of utterances. Thus, a word typically includes one or more phonemes. For purposes of simplicity, phonemes may be thought of as utterances of letters, but this is not a perfect analogy, as some phonemes may present multiple letters. An example phonemic spelling for the American English pronunciation of the word “cat” is /k/ /ae/ /t/, consisting of the phonemes /k/, /ae/, and /t/. Another example phonemic spelling for the word “dog” is /d/ /aw/ /g/, consisting of the phonemes /d/, /aw/, and /g/. 
     Different phonemic alphabets exist, and these alphabets may have different textual representations for the various phonemes therein. For example, the letter “a” may be represented by the phoneme /ae/ for the sound in “cat,” by the phoneme /ey/ for the sound in “ate,” and by the phoneme /ah/ for the sound in “beta.” Other phonemic representations are possible. 
     Common phonemic alphabets for American English contain about 40 distinct phonemes. Each of these phonemes may be associated with a different distribution of feature vector values. Thus, acoustic model  408  may be able to estimate the phoneme(s) in a feature vector by comparing the feature vector to the distributions for each of the 40 phonemes, and finding one or more phonemes that are most likely represented by the feature vector. 
     One way of doing so is through use of a hidden Markov model (HMM). An HMM may model a system as a Markov process with unobserved (i.e., hidden) states. Each HMM state may be represented as a multivariate Gaussian distribution that characterizes the statistical behavior of the state. Additionally, each state may also be associated with one or more state transitions that specify the probability of making a transition from the current state to another state. 
     When applied to an ASR system, the combination of the multivariate Gaussian distribution and the state transitions for each state may define a time sequence of feature vectors over the duration of one or more phonemes. Alternatively or additionally, the HMM may model the sequences of phonemes that define words. Thus, some HMM-based acoustic models may also consider phoneme context when a mapping a sequence of feature vectors to one or more words. 
       FIG. 5  depicts an example HMM-based acoustic model  500 . Acoustic model  500  defines a sequence of phonemes that make up the word “cat.” Thus, each phoneme is represented by a 3-state HMM with an initial state, a middle state, and an end state representing the statistical characteristics at the beginning of phoneme, the middle of the phoneme, and the end of the phoneme, respectively. Each state (e.g., state /k/1, state /k/2, etc.) may represent a phoneme and may include one or more transitions. 
     Acoustic model  500  may represent a word by concatenating the respective 3-state HMMs for each phoneme in the word together, with appropriate transitions. These concatenations may be performed based on information in dictionary  410 , as discussed below. In some implementations, more or fewer states per phoneme may be used in an acoustic model. 
     An acoustic model may be trained using recordings of each phoneme in numerous contexts (e.g., various words and sentences) so that a representation for each of the phoneme&#39;s states can be obtained. These representations may encompass the multivariate Gaussian distributions discussed above. 
     In order to train the acoustic model, a possibly large number of utterances containing spoken phonemes may each be associated with transcriptions. These utterances may be words, sentences, and so on, and may be obtained from recordings of everyday speech or some other source. The transcriptions may be high-accuracy automatic or manual (human-made) text strings of the utterances. 
     The utterances may be segmented according to their respective transcriptions. For instance, training of the acoustic models may involve segmenting the spoken strings into units (e.g., using either a Baum-Welch and/or Viterbi alignment method), and then using the segmented utterances to build distributions for each phoneme state. 
     Consequently, as more data (utterances and their associated transcriptions) are used for training, a more accurate acoustic model is expected to be produced. However, even a well-trained acoustic model may have limited accuracy when used for ASR in a domain for which it was not trained. For instance, if an acoustic model is trained by utterances from a number of speakers of American English, this acoustic model may perform well when used for ASR of American English, but may be less accurate when used for ASR of, e.g., British English. 
     Also, if an acoustic model is trained using utterances from a number of speakers, it will likely end up representing each phoneme as a statistical average of the pronunciation of this phoneme across all of the speakers. Thus, an acoustic model trained in this fashion may represent the pronunciation and usage of a hypothetical average speaker, rather than any particular speaker. 
     For purposes of simplicity, throughout this specification and the accompanying drawings, it is assumed that acoustic models represent phonemes as context-dependent phonemic sounds. However, acoustic models that use other types of representations are within the scope of the embodiments herein. 
     D. Dictionary 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Word 
                 Phonemic Interpretation 
               
               
                   
                   
               
             
            
               
                   
                 cat 
                 /k/ /ae/ /t/ 
               
               
                   
                 and 
                 /ay/ /n/ /d/ 
               
               
                   
                 dog 
                 /d/ /aw/ /g/ 
               
               
                   
                   
               
            
           
         
       
     
     As noted above, dictionary  410  may define a pre-established mapping between phonemes and words. This mapping may include a list of tens or hundreds of thousands of phoneme-pattern-to-word mappings. Thus, in some embodiments, dictionary  410  may include a lookup table, such as Table 1. Table 1 illustrates how dictionary  410  may list the phonemic sequences that pattern classification module  406  uses for the words that the ASR system is attempting to recognize. Therefore, dictionary  410  may be used when developing the phonemic state representations of words that are illustrated by acoustic model  500 . 
     E. Language Model 
     Language model  412  may assign probabilities to sequences of phonemes or words, based on the likelihood of that sequence of phonemes or words occurring in an input utterance to the ASR system. Thus, for example, language model  412  may define the conditional probability of w n  (the nth word in a phrase transcribed from an utterance), given the values of the pattern of n−1 previous words in the phrase. More formally, language model  412  may define
 
 P ( w   n   |w   1   , w   2   , . . . , w   n-1 )
 
     In general, a language model may operate on n-grams, which, for example, may be sequences of n phonemes or words that are represented in pattern classification module  406 . In practice, language models with values of n greater than 5 are rarely used because of their computational complexity, and also because smaller n-grams (e.g., 3-grams, which are also referred to as tri-grams) tend to yield acceptable results. In the example described below, tri-grams are used for purposes of illustration. Nonetheless, any value of n may be may be used with the embodiments herein. 
     Language models may be trained through analysis of a corpus of text strings. This corpus may contain a large number of words, e.g., hundreds, thousands, millions or more. These words may be derived from utterances spoken by users of an ASR system and/or from written documents. For instance, a language model can be based on the word patterns occurring in human speech, written text (e.g., emails, web pages, reports, academic papers, word processing documents, etc.), and so on. 
     From such a corpus, tri-gram probabilities can be estimated based on their respective number of appearances in the training corpus. In other words, if C(w 1 , w 2 , w 3 ) is the number of occurrences of the word pattern w 1 , w 2 , w 3  in the corpus, then 
     
       
         
           
             
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     Thus, a language model may be represented as a table of conditional probabilities. Table 2 illustrates a simple example of such a table that could form the basis of language model  406 . Particularly, Table 2 contains tri-gram conditional probabilities. 
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Tri-gram Conditional Probabilities 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                   
                 P(dog|cat,and) = 0.50 
               
               
                   
                 P(mouse|cat,and) = 0.35 
               
               
                   
                 P(bird|cat,and) = 0.14 
               
               
                   
                 P(fiddle|cat,and) = 0.01 
               
               
                   
                   
               
            
           
         
       
     
     For the 2-gram prefix “cat and,” Table 2 indicates that, based on the observed occurrences in the corpus, 50% of the time the next 1-gram is “dog.” Likewise, 35% of the time, the next 1-gram is “mouse,” 14% of the time the next 1-gram is “bird,” and 1% of the time the next 1-gram is “fiddle.” Clearly, in a fully-trained ASR system, the language model would contain many more entries, and these entries would include more than just one 2-gram prefix. 
     Nonetheless, using the observed frequencies of word patterns from a corpus of speech (and/or from other sources) is not perfect, as some acceptable tri-grams may not appear in the corpus, and may therefore be assigned a probability of zero. Consequently, when given a zero-probability tri-gram at run time, the language model may instead attempt to map this tri-gram to a different tri-gram associated with a non-zero probability. 
     In order to reduce this likelihood, the language model may be smoothed so that zero-probability tri-grams have small non-zero probabilities, and the probabilities of the tri-grams in the corpus are reduced accordingly. In this way, tri-grams not found in the corpus can still be recognized by the language model. 
     4. Example Automatic Speech Recognition System Operation 
     Once acoustic model  408  and language model  412  are appropriately trained, feature analysis model  402  and pattern classification module  406  may be used to perform ASR. Provided with an input utterance, the ASR system can search the space of valid word sequences from the language model to find the word sequence with the maximum likelihood of having been spoken in the utterance. A challenge with doing so is that the size of the search space can be quite large, and therefore performing this search may take an excessive amount computing resources (e.g., processing time and memory utilization). Nonetheless, there are some heuristic techniques that can be used to reduce the complexity of the search, potentially by orders of magnitude. 
     For instance, a finite state transducer (FST) can be used to compactly represent multiple phoneme patterns that map to a single word. Some words, such as “data,” “either,” “tomato,” and “potato,” have multiple pronunciations. The phoneme sequences for these pronunciations can be represented in a single FST per word. 
     This process of creating efficient phoneme-level FSTs can be carried out for each word in dictionary  410 , and the resulting word FSTs can be combined into sentence FSTs using the language model  412 . Ultimately, a very large network of states for phonemes, words, and sequences of words can be developed and represented in a compact search graph. 
       FIG. 6  contains an example search graph  600 . In order to be illustrative, search graph  600  is much smaller and less complex than a search graph that would be used in an actual ASR system. Particularly, search graph  600  was trained with only the five input utterances, “catapult,” “cat and mouse,” “cat and dog,” “cat,” and “cap.” 
     Each circle in search graph  408  may represent a state associated with the processing of an input utterance that has been mapped to phonemes. For purposes of simplicity, each phoneme in search graph  600  is represented with a single state rather than multiple states. Also, self-transitions are omitted from search graph  600  in order to streamline  FIG. 6 . 
     The states in search graph  600  are named based on the current phoneme context of the input utterance, using the format “x[y]z” to indicate that the current phoneme being considered, y, has a left-context of the phoneme x and a right context of the phoneme z. In other words, the state “x[y]z” indicates a point in processing an utterance in which the current phoneme being considered is y, the previously phoneme in the utterance is x, and the next phoneme in the utterance is z. The beginning of an utterance and the end of an utterance are represented by the “#” character, and also may be referred to as null phonemes. 
     Terminal states may be represented by a recognized word or phrase in quotes. Search graph  600  includes five terminal states, representing recognition of the words or phrases “catapult,” “cat and mouse,” “cat and dog,” “cat,” and “cap.” 
     Transitions from one state to another may represent an observed ordering of phonemes in the corpus. For instance, the state “#[k]ae” represents the recognition of a “k” phoneme with a left context of a null phoneme and a right context of an “ae” phoneme. There are two transitions from the state “#[k]ae”—one for which the next phoneme (the phoneme after the “ae”) is a “t” and another for which the next phoneme is a “p.” 
     Based on acoustic model  408 , dictionary  410 , and language model  412 , costs may be assigned to one or more of the states and/or transitions. For example, if a particular phoneme pattern is rare, a transition to a state representing that phoneme pattern may have a higher cost than a transition to a state representing a more common phoneme pattern. Similarly, the conditional probabilities from the language model (see Table 2 for examples) may also be used to assign costs to states and/or transitions. For instance, in Table 2, given a phrase with the words “cat and,” the conditional probability of the next word in the phrase being “dog” is 0.5, while the conditional probability of the next word in the phrase being “mouse” is 0.35. Therefore, the transition from state “ae[n]d” to state “n[d]m” may have a higher cost than the transition from state “ae[n]d” to state “n[d]d.” 
     Search graph  600 , including any states, transitions between states, and associated costs therein, may be used to estimate text string transcriptions for new input utterances. For example, pattern classification module  406  may determine a sequence of one or more words that match an input utterance based on search graph  600 . Formally, pattern classification module  406  may attempt to find
 
 w *=argmax w   P (α| w ) P ( w )
 
where α is a stream of feature vectors derived from the input utterance, P(α|w) represents the probability of those feature vectors being produced by a word sequence w, and P(w) is the probability assigned to w by language model  412 . For example, P(w) may be based on n-gram conditional probabilities as discussed above, as well as other factors. The function argmax w  may return the value of w that maximizes P(α|w)P(w).
 
     To find text strings that may match utterance  400 , pattern classification module  406  may attempt to find paths from an initial state in search graph  600  to a terminal state in search graph  600  based on feature vectors  404 . This process may involve pattern classification module  406  performing a breadth-first search, depth-first search, beam search, or some other type of search on search graph  600 . Pattern classification module  406  may assign a total cost to one or more paths through search graph  600  based on costs associated with the states and/or transitions of associated with each path. Some of these costs may be based on, for instance, a confidence level that a particular segment of the utterance maps to a particular sequence of phonemes in the path. 
     As an example, suppose that utterance  400  is the phrase “cat and dog.” In a possible scenario, pattern classification module  406  would step through search graph  600  phoneme by phoneme to find the path beginning with initial state “#[k]ae” and ending with terminal state “cat and dog.” Pattern classification module  406  may also find one or more additional paths through search graph  600 . For example, pattern classification module  406  may also associate utterance  400  with the path with initial state “#[k]ae” and ending with terminal state “cat and mouse,” and with the path with initial state “#[k]ae” and ending with terminal state “catapult.” Nonetheless, pattern classification module  406  may assign a lower cost to the path with terminal state “cat and dog” than to other paths. Consequently, the path with terminal state “cat and dog” may be selected as the “best” transcription for the input utterance. 
     It should be understood that ASR systems can operated in many different ways. The embodiments described above are presented for purposes of illustration and may not be the only way in which an ASR system operates. 
     5. Examples of Multi-Stage Speaker Adaptation 
     As noted above, acoustic models are typically trained with utterances from multiple speakers in multiple environments. As a result, a given acoustic model may represent a hypothetical average speaker, and might not perform well when applied to utterances from a speaker whose vocal characteristics differ from those of the hypothetical average speaker. Therefore, ASR systems may attempt to compensate for these differences through speaker adaptation. 
     A. Speaker Adaptation Profiles 
       FIG. 7  depicts an example ASR system with speaker adaptation. Particularly, an utterance is provided as input to feature analysis module  400 , which produces one or more feature vectors based on the utterance. These feature vectors may be provided to speaker adaptation module  700 , which may modify the feature vectors according to one or more of speaker adaptation profile(s)  702 . The modified feature vectors may be provided to pattern classification module  406 , which in turn may produce one or more text string transcriptions of the utterance. 
     Speaker adaptation profiles  702  may include default, gender-specific, and/or speaker-dependent profiles. A default profile may be a profile that the ASR system uses when no other profile has been selected. For instance, when the ASR system begins speech recognition, it may apply the default profile to feature vectors. Additionally, the default profile may be applied to utterances received after the ASR system has been idle for some period of time (e.g., 1-10 minutes or more) or after the ASR system detects that the speaker has changed. In some embodiments, the default profile may not perform any speaker adaptation—in other words, for some default profiles, the modified feature vectors may be the same as the feature vectors. 
     Speaker-adaptation profiles  702  may also include one or more environment-specific, speaker-dependent profiles. Each of these profiles may be associated with a particular speaker speaking in a particular environment or location. For example, one such profile might be based on the particular speaker speaking in a quiet location, with little background noise. Another such profile might be based on the particular speaker speaking in an environment with a given type of background noise, such as an office or a car. Thus, an environment-specific, speaker-dependent speaker adaptation profile for the speaker may be based on the characteristics of the input utterances, the speaker&#39;s location, and or the user device that receives the utterance. 
     As noted above, each of the feature vectors may be of a particular length (e.g., n entries), and may include representations of the temporal and/or spectral acoustic features of at least a portion of the utterance. In some embodiments, the speaker adaptation parameters may take the form of a matrix, for instance, an n×n matrix. In order to perform speaker adaptation, speaker adaptation module  700  may multiply each feature vector it receives by the matrix, resulting in updated feature vectors. These updated feature vectors may be transmitted to pattern classification module  406 . 
     The acoustic model used by pattern classification module  406  may be speaker-independent, and speaker adaptation module  700  may use the matrix to adapt speaker-dependent feature vectors so that they are more likely to be properly recognized by the acoustic model. In some cases, the matrix may be a diagonal matrix (i.e., for each entry (i,j) in the matrix, the entry takes on a non-zero value if i is equal to j, but takes on a value of zero if i is not equal to j). Since at least half of the entries in a 2×2 or greater diagonal matrix contain zeroes, less computation is required to multiply a feature vector by a diagonal matrix than a non-diagonal matrix. (Herein, a non-diagonal matrix refers to a matrix in which at least one entry for which i is not equal to j contains a non-zero value.) 
     B. Gaussian Mixture Models as Speech Models 
     From time to time, periodically, and/or on an ongoing basis, the ASR system may compare the characteristics of received utterances to speech models associated with one or more speaker adaptation profiles. Based on the outcome of this comparison, a new speaker adaptation profile may be selected, or the current speaker adaptation profile may continue to be applied. 
     In some embodiments, the speech models may be represented as Gaussian mixture models (GMMs). A GMM may probabilistically represent the likelihood that a particular speaker is speaking based on feature vectors derived from an input utterance. Formally, a GMM may be a weighted sum of M Gaussian random variables, each with potentially different mean and covariance parameters. An example GMM is given by the equation 
                 p   ⁡     (       x   |     w   i       ,     μ   i     ,     Σ   i       )       =       ∑     i   =   1     M     ⁢       w   i     ⁢     g   ⁡     (       x   |     μ   i       ,     Σ   i       )             ,     i   =     1   ⁢           ⁢   …   ⁢           ⁢   M             
where x is an n-dimensional feature vector, w i  are weights such that Σ i=1   M  w i =1, and g(x|μ i , Σ i ) is an n-dimensional Gaussian function with a mean of μ i  and a covariance matrix of Σ i . The speech model for a given profile may be represented as
 
λ={ w   i ,μ i ,Σ i   }, i =1  . . . M  
 
     GMMs can be used to approximate arbitrarily-shaped probability density functions. Thus, GMMs are powerful tools for representing distributions of feature vectors in ASR systems. In some implementations, full covariance matrices are not used, as partial covariance matrices (e.g., diagonal matrices wherein each non-zero entry represents the variance of a particular component Gaussian function) can provide suitable results. 
     C. Profile Selection 
     Assume that the ASR system has access to a set of S speaker adaptation profiles represented by speech models λ 1 , λ 2 , . . . , λ S , respectively. A goal of speaker adaptation is to select the profile, Ŝ, with a speech model that has the maximum a posteriori probability of being the closest fit for a series of feature vectors. Formally, 
               S   ^     =       arg   ⁢           ⁢       max     1   ≤   k   ≤   S       ⁢     p   ⁡     (       λ   k     |   X     )           =     arg   ⁢           ⁢       max     1   ≤   k   ≤   S       ⁢       ∑     t   =   1     T     ⁢     log   ⁢           ⁢     p   ⁡     (       x   t     ,     λ   k       )                       
where X is a series of T feature vectors, x t , 1≦t≦T. The final equation can be derived from argmax 1≦k≦S  p (λ k |X) through application of Bayes Rule and some simplifying assumptions. Note that p(x t |λ k )=p(x t |w k , μ k , Σ k ), and thus the solution to this term is provided by the equations in Section 5B.
 
     When selecting a profile, any value of T may be used. For instance, assuming that a feature vector is derived from 10 milliseconds of an input utterance, anywhere from one to several thousand feature vectors may be evaluated according to the equations above, and a profile that fits a majority of the feature vectors may be selected. 
     D. Speech Model Training 
     A speech model for a particular speaker adaptation profile (e.g., for speakers of a specific gender, a particular speaker, and/or a particular speaker in a specific environment or location) may be trained based on input utterances. For instance, a female-specific speech model may be trained with utterances from various female speakers in various environments. A speaker-dependent speech model may be trained with utterances from a particular speaker in various environments. An environment-specific, speaker-dependent speech model may be trained with utterances from a particular speaker in a particular environment. 
     One way of conducting this training is to iteratively calculate a maximum likelihood estimate of a GMM given T observed feature vectors using an expectation-maximization technique. Particularly, this technique provides estimates of the parameters λ={w i , μ i , Σ i }. Formally, 
     
       
         
           
             
               
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     Note that, for sake of simplicity, these equations only calculate the variances, σ i   2 , rather than the full covariance matrix, Σ i . However, as noted above, these variances can be used to form a diagonal covariance matrix that is sufficient for this example embodiment. 
     E. Example Embodiments of Speaker Adaptation 
       FIG. 8A  is a message flow illustrating an example embodiment of speaker adaptation. This embodiment involves feature analysis module  402 , speaker adaptation module  700 , and pattern classification module  406 . However, speaker adaptation may be performed entirely by speaker adaptation module  700 , and other modules may not be aware that speaker adaptation is occurring. Alternatively, speaker adaptation may involve other modules. For instance, some embodiments of speaker adaptation may adapt the acoustic model of pattern classification module  406  to the speech characteristics of one or more speakers. 
     Additionally, the modules in  FIG. 8A  may be contained in a single computing device (e.g., a tablet computer, personal computer, or wireless communication device), or may be distributed between two or more devices (e.g., between a client device and a server device). Furthermore, it is assumed that speaker adaptation module  700  has access to one or more speaker adaptation profiles. 
     At step  800 , feature analysis module  402  may receive an utterance. At step  802 , one or more feature vectors derived from this utterance may be provided to speaker adaptation module  700 . 
     In some embodiments, when speaker adaptation module  700  receives these feature vectors, speaker adaptation module  700  might not be configured for speaker adaptation (e.g., speaker adaptation module  700  may be configured to not apply speaker adaptation to received feature vectors). Thus, at step  804 , the unmodified feature vectors may be provided to pattern classification module  406 . However, in other embodiments, speaker adaptation module  700  may be configured to apply a particular speaker adaptation technique to input feature vectors. Regardless, at step  806 , pattern classification module  406  may provide one or more text strings as ASR system output. These text strings may represent transcriptions of utterance  800 . 
     At step  808 , possibly based on the characteristics of the feature vectors provided in step  802 , speaker adaptation module  700  may select a speaker adaptation profile associated with a gender-specific speaker adaptation technique. The selection process may involve speaker adaptation module  700  comparing these feature vectors to one or more speaker adaptation profiles, choosing a speaker adaptation profile that fits the characteristics of the feature vectors, and applying the associated speaker adaptation technique to subsequently-received feature vectors. 
     As noted above, for small samples of an utterance, and/or for utterances of a short duration, the characteristics of feature vectors from these utterances may not sufficiently fit speaker-dependent speaker adaptation profiles. Thus, prior to identifying a particular individual as the speaker and applying that speaker&#39;s speaker adaptation profile, speaker adaptation module  700  may estimate the gender of the speaker and apply a gender-specific speaker adaptation technique based on the estimated gender. 
     In addition to or instead of comparing the characteristics of feature vectors to speaker adaptation profiles, estimating the gender of a speaker may involve comparing the frequencies represented by the feature vectors to a frequency threshold. If the frequencies represented by the feature vectors are generally above this frequency threshold, the speaker may be estimated to be female. If the frequencies represented by the feature vectors are generally below the frequency threshold, the speaker may be estimated to be male. The frequency threshold may vary based on the phoneme represented by the feature vectors. Therefore, step  808  may involve speaker adaptation module  700  selecting either a male-specific speaker adaptation technique or a female-specific speaker adaptation technique based on the received feature vectors. 
     At step  810 , additional feature vectors may be provided by feature analysis module  402 . These feature vectors may be derived from the utterance of step  800  or some other utterance. Speaker adaptation module  700  may apply the selected gender-specific speaker adaptation technique to these feature vectors to produce modified feature vectors. At step  812 , speaker adaptation module  700  may provide these modified feature vectors to pattern classification module  406 . At step  814 , pattern classification module  406  may provide one or more text strings as ASR system output. These text strings may be based on the application of the selected gender-specific speaker adaptation technique to these feature vectors. 
     Additionally, at step  816 , speaker adaptation module  700  may select a speaker adaptation profile associated with a speaker-dependent speaker adaptation technique. Thus, speaker adaptation module  700  may compare the feature vectors of step  810  (and possibly the feature vectors of step  802  as well) to one or more speaker adaptation profiles. Provided that speaker adaptation module  700  has received a sufficient number of feature vectors to identify the speaker, speaker adaptation module  700  may choose a speaker adaptation profile that fits the characteristics of the feature vectors. This process may include use of the equations discussed in Section 5C. 
     At step  818 , more feature vectors may be provided by feature analysis module  402 . These feature vectors also may be derived from the utterance of step  800  and/or some other utterance. Speaker adaptation module  700  may apply the selected speaker-dependent speaker adaptation technique to these feature vectors to produce modified feature vectors. At step  820 , speaker adaptation module  700  may provide these modified feature vectors to pattern classification module  406 . At step  822 , pattern classification module  406  may provide one or more text strings as ASR system output. These text strings may be based on the application of the selected speaker-dependent speaker adaptation technique to these feature vectors. 
     Additionally, at step  824 , speaker adaptation module  700  may select a speaker adaptation profile associated with an environment-specific, speaker-dependent speaker adaptation technique. Thus, speaker adaptation module  700  may compare the feature vectors of step  818  (and possibly the feature vectors of steps  802  and  810  as well) to one or more speaker adaptation profiles of the selected speaker. Presuming that speaker adaptation module  700  has received a sufficient number of feature vectors to identify the environment in which the speaker is speaking, speaker adaptation module  700  may choose a speaker adaptation profile that fits the characteristics of the feature vectors. This process may also include use of the equations discussed in Section 5C. 
       FIG. 8A  illustrates a speaker adaptation module employing a multi-stage process to refine speaker adaptation from a general technique (e.g., no speaker adaptation), to a gender-specific technique, then to a speaker-dependent technique, and then to an environment-specific, speaker-dependent technique. However, multi-stage speaker identification may refine speaker adaptation in other ways. Particularly, as the speaker providing utterances changes, a speaker adaptation module may switch between two or more speaker-dependent speaker adaptation techniques. Alternatively or additionally, the speaker adaptation module may switch from using a speaker-dependent technique to a speaker-independent technique or a gender-specific technique, and/or from an environment-specific, speaker-dependent technique to a speaker-independent technique, a gender-specific technique, or a speaker-dependent technique. 
       FIG. 8B  is another message flow illustrating an example embodiment of speaker adaptation. This embodiment also involves feature analysis module  402 , speaker adaptation module  700 , and pattern classification module  406 . However, like the example embodiment of  FIG. 8A , more or fewer modules may be included in speaker adaptation procedures, and the modules may be contained in a single computing device or distributed between two or more devices. Again, it is assumed that speaker adaptation module  700  has access to one or more speaker adaptation profiles. For instance, these speaker adaptation profiles may include a male-specific speaker adaptation profile, a female-specific speaker adaptation profile, a speaker-dependent speaker-adaptation profile for male speaker A, and a speaker-dependent speaker-adaptation profile for female speaker B. 
     At step  850 , feature analysis module  402  may receive an utterance. It is assumed that this utterance was made by male speaker A. At step  852 , feature analysis module  402  may provide one or more feature vectors derived from this utterance to speaker adaptation module  700 . 
     In some embodiments, when speaker adaptation module  700  receives these feature vectors, speaker adaptation module  700  might not be configured for speaker adaptation. Thus, at step  854 , the unmodified feature vectors may be provided to pattern classification module  406 . However, in other embodiments, speaker adaptation module  700  may be configured to apply a particular speaker adaptation technique to input feature vectors. Regardless, at step  856 , pattern classification module  406  may provide one or more text strings as ASR system output. These text strings may represent transcriptions of utterance  850 . 
     At step  858 , possibly based on the characteristics of the feature vectors provided in step  852 , speaker adaptation module  700  may select a speaker adaptation profile associated with a speaker-dependent speaker adaptation technique. The selection process may involve speaker adaptation module  700  comparing these feature vectors to one or more speaker adaptation profiles, choosing a speaker adaptation profile that fits the characteristics of the feature vectors, and applying the associated speaker adaptation technique to subsequently-received feature vectors. In this case, speaker adaptation module  700  may select a speaker-dependent speaker adaptation profile for male speaker A. Consequently, speaker adaptation module  700  may apply an associated speaker-dependent speaker adaptation technique to received feature vectors while this profile is selected. 
     In some embodiments, step  858  may be implemented in multiple discrete steps. For example, speaker adaptation module  700  may first select a gender-specific speaker adaptation profile, apply this profile to at least some feature vectors, then select the speaker-dependent speaker adaptation profile for male speaker A. 
     At step  860 , feature analysis module  402  may receive another utterance. It is assumed that this utterance was made by female speaker B. At step  862 , feature analysis module  402  may provide one or more feature vectors derived from this utterance to speaker adaptation module  700 . 
     Since the speaker-dependent speaker adaptation technique for male speaker A is being applied, at step  864  speaker adaptation module  700  provides modified feature vectors. At step  866 , pattern classification module  406  may provide one or more text strings as ASR system output. These text strings may represent transcriptions of utterance  860 . 
     At step  868 , possibly based on the characteristics of the feature vectors provided in step  862 , speaker adaptation module  700  may select a speaker adaptation profile associated with a gender-specific speaker adaptation technique. The selection process may involve speaker adaptation module  700  comparing the feature vectors of step  862  to one or more speaker adaptation profiles, choosing a speaker adaptation profile that best fits the characteristics of the feature vectors, and applying the associated speaker adaptation technique to subsequently-received feature vectors. In this case, speaker adaptation module  700  may select a gender-specific speaker adaptation profile for female speakers. Consequently, speaker adaptation module  700  may apply an associated gender-specific speaker adaptation technique to received feature vectors while this profile is selected. 
     At step  870 , more feature vectors may be provided by feature analysis module  402 . These feature vectors may be derived from the utterance of step  860  or a subsequently-received utterance. Speaker adaptation module  700  may apply the selected gender-specific speaker adaptation technique to these feature vectors to produce modified feature vectors. At step  872 , speaker adaptation module  700  may provide these modified feature vectors to pattern classification module  406 . At step  874 , pattern classification module  406  may provide one or more text strings as ASR system output. These text strings may be based on the application of the selected gender-specific speaker adaptation technique to the modified feature vectors. 
     Additionally, at step  876 , speaker adaptation module  700  may select a speaker adaptation profile associated with a speaker-dependent speaker adaptation technique. Thus, speaker adaptation module  700  may compare the feature vectors of step  870  (and possibly the feature vectors of step  862  as well) to one or more speaker adaptation profiles. Presuming that speaker adaptation module  700  has received a sufficient number of feature vectors to identify the speaker, speaker adaptation module  700  may choose a speaker adaptation profile that fits the characteristics of the feature vectors. Thus, speaker adaptation module  700  may select a speaker-dependent speaker adaptation profile for female speaker B. An associated speaker adaptation technique may be applied to subsequently-received feature vectors. 
     Further, an environment-specific, speaker-dependent speaker adaptation technique for female speaker B may be subsequently applied. Alternatively or additionally, the speaker providing utterances may change back to male speaker A or some other speaker, and speaker adaptation module  700  may select a new speaker adaptation profile accordingly. 
     It should be understood that the embodiments illustrated in  FIGS. 7 ,  8 A, and  8 B are for purposes of example. Other embodiments may be possible, including variations that change the order and/or content of the steps of  FIGS. 7 ,  8 A, and  8 B. In some embodiments, more or fewer steps may be employed. Further, the embodiments of  FIGS. 7 ,  8 A, and  8 B may be combined, in part or in whole. For instance, one or more steps of the embodiment of  FIG. 8B  could occur before, during, or after steps in the embodiment of  FIG. 8A  are carried out. 
     6. Example Operations 
       FIG. 9  is a flow chart of an example embodiment. The steps illustrated by this flow chart may be carried out by various computing devices, such as client device  300 , server device  200  and/or server cluster  220 A. Aspects of some individual steps may be distributed between multiple computing devices. 
     At step  900 , a first gender-specific speaker adaptation technique may be selected based on characteristics of a first set of feature vectors. The first set of feature vectors may correspond to a first unit of input speech. The first set of feature vectors may also be configured for use in ASR of the first unit of input speech. Here, a unit of speech may be an utterance, part of an utterance, or more than one utterance. Thus, each unit of speech may have been made by the same speaker or by different speakers. 
     At step  902 , a second set of feature vectors may be modified based on the first gender-specific speaker adaptation technique. The second set of feature vectors may correspond to a second unit of input speech. The modified second set of feature vectors may be configured for use in ASR of the second unit of input speech. 
     At step  904 , a first speaker-dependent speaker adaptation technique may be selected based on characteristics of the second set of feature vectors. Selecting the first speaker-dependent speaker adaptation technique may involve determining that the characteristics of the second set of feature vectors fit a speaker-dependent speech model associated with the first speaker-dependent speaker adaptation technique better than the characteristics of the second set of feature vectors fit one or more additional speaker-dependent speech models. In some embodiments, the first gender-specific speaker adaptation technique may be associated with a particular gender, and the first speaker-dependent speaker adaptation technique may be associated with a speaker of the particular gender. 
     Determining whether characteristics of feature vectors fit one or more speech models may include applying the GMM-based techniques discussed in Section 5. In some cases, the equations described in Section 5C may be used to select a speech model that appropriately fits the characteristics of the feature vectors. 
     At step  906 , a third set of feature vectors may be modified based on the first speaker-dependent speaker adaptation technique. The third set of feature vectors may correspond to a third unit of input speech. The modified third set of feature vectors may be configured for use in ASR of the third unit of input speech. 
     Modifying the second set of feature vectors may involve applying a first gender-specific transform to feature vectors in the second set of feature vectors. The first gender-specific transform may be associated with the first gender-specific speaker adaptation technique. Modifying the third set of feature vectors may involve applying a first speaker-dependent transform to feature vectors in the third set of feature vectors. The first speaker-dependent transform may be associated with the first speaker-dependent speaker adaptation technique. 
     In some embodiments, a particular speaker may be associated with the first speaker-dependent speaker adaptation technique. In these embodiments, it may be determined that (i) the third unit of input speech was originated proximate to a particular location, and (ii) the particular speaker may also be associated with an environment-specific, speaker-dependent speaker adaptation technique. Further, the environment-specific, speaker-dependent speaker adaptation technique may be associated with the particular location. Thus, the environment-specific, speaker-dependent speaker adaptation technique may be selected, and a fourth set of feature vectors may be modified based on the environment-specific, speaker-dependent speaker adaptation technique. The fourth set of feature vectors may correspond to a fourth unit of input speech. The modified fourth set of feature vectors may be configured for use in ASR of the fourth unit of input speech. 
     Additionally or alternatively, a first speaker may be associated with the first speaker-dependent speaker adaptation technique. A second speaker-dependent speaker adaptation technique may be selected based on characteristics of the third set of feature vectors, where a second speaker is associated with the second speaker-dependent speaker adaptation technique. 
     A fourth set of feature vectors may be modified based on the second speaker-dependent speaker adaptation technique. The fourth set of feature vectors may correspond to a fourth unit of input speech. The modified fourth set of feature vectors may be configured for use in ASR of the fourth unit of input speech. 
     Furthermore, the first gender-specific speaker adaptation technique may be associated with a speech model of a first gender, and a second gender-specific speaker adaptation technique may be associated with a speech model of a second gender. Selecting the first gender-specific speaker adaptation technique may involve determining that the characteristics of the first set of feature vectors fit the speech model of the first gender better than the speech model of the second gender. 
     Moreover, it may be determined that (i) characteristics of the third set of feature vectors fit the speech model of the second gender better than the speech model of the first gender, and (ii) the characteristics of the third set of feature vectors fit the speech model of the second gender better than speech model of the first speaker-dependent speaker adaptation technique. Perhaps in response to making this determination, the second gender-specific speaker adaptation technique may be selected. 
     Accordingly, a fourth set of feature vectors may be modified based on the second gender-specific speaker adaptation technique. The fourth set of feature vectors may correspond to a fourth unit of input speech. The modified fourth set of feature vectors may be configured for use in ASR of the fourth unit of input speech. 
       FIGS. 10A and 10B  are another flow chart of an example embodiment. Like the example embodiment of  FIG. 9 , the steps illustrated by this flow chart may be carried out by various computing devices, such as client device  300 , server device  200  and/or server cluster  220 A, and aspects of some individual steps may be distributed between multiple computing devices. 
     At step  1000 , a first set of feature vectors may be obtained. The first set of feature vectors may correspond to a first unit of input speech. At step  1002 , characteristics of the first set of feature vectors may be compared to a first gender-specific speech model and a second gender-specific speech model. One of these speech models may be male-specific and the other may be female-specific, or both may be different speech models for the same gender, but not necessarily adapted to a particular individual. 
     At step  1004 , the characteristics of the first set of feature vectors may be determined to fit the first gender-specific speech model better than the second gender-specific model. At step  1006 , a second set of feature vectors may be obtained. The second set of feature vectors may correspond to a second unit of input speech. 
     At step  1008 , the second set of feature vectors may be modified based on a first gender-specific speaker adaptation technique associated with the first gender-specific speech model. Modifying the second set of feature vectors may involve applying a first gender-specific transform to feature vectors in the second set of feature vectors. The first gender-specific transform may be associated with the first gender-specific speaker adaptation technique. 
     At step  1010 , after modifying the second set of feature vectors, characteristics of the second set of feature vectors may be compared to the first gender-specific speech model, the second gender-specific speech model, and at least one speaker-dependent speech model. At step  1012 , it may be determined that the characteristics of the second set of feature vectors fit the speaker-dependent speech model better than the first and second gender-specific models. At step  1014 , a third set of feature vectors may be obtained. The third set of feature vectors may correspond to a third unit of input speech. 
     At step  1016  the third set of feature vectors may be modified based on a speaker-dependent speaker adaptation technique associated with the speaker-dependent speech model. Modifying the third set of feature vectors may involve applying a speaker-dependent transform to feature vectors in the third set of feature vectors. The speaker-dependent transform may be associated with the speaker-dependent speaker adaptation technique 
     In some embodiments, after modifying the third set of feature vectors, the characteristics of the third set of feature vectors may be compared to the first gender-specific speech model, the second gender-specific speech model, the speaker-dependent speech model, and at least one environment-specific, speaker-dependent speech model. The speaker-dependent speech model and the environment-specific, speaker-dependent speech model may both be associated with a particular speaker. Possibly as a result of the comparison, it may be determined that the characteristics of the third set of feature vectors fit the environment-specific, speaker-dependent speech model better than the speaker-dependent speech model and both of the first and second gender-specific models. 
     A fourth set of feature vectors may be obtained, and the fourth set of feature vectors may be modified based on an environment-specific, speaker-dependent speaker adaptation technique associated with the environment-specific, speaker-dependent speech model. The fourth set of feature vectors may correspond to a fourth unit of input speech. 
     Alternatively or additionally, the first gender-specific speaker adaptation technique may be associated with a particular gender, and the speaker-dependent speaker adaptation technique may be associated with a speaker of the particular gender. Selecting the speaker-dependent speaker adaptation technique may be based on the first gender-specific speaker adaptation technique being associated with a particular gender. 
     7. Conclusion 
     The above detailed description describes various features and functions of the disclosed systems, devices, and methods with reference to the accompanying figures. In the figures, similar symbols typically identify similar components, unless context indicates otherwise. The illustrative embodiments described in the detailed description, figures, and claims are not meant to be limiting. Other embodiments can be utilized, and other changes can be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein. 
     In situations in which the systems discussed here collect personal information about users, or may make use of personal information, the users may be provided with an opportunity to control whether programs or features collect user information (e.g., information about a user&#39;s social network, social actions or activities, profession, a user&#39;s preferences, or a user&#39;s current location), or to control whether and/or how to receive content from the system that may be more relevant to the user. In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user&#39;s identity may be treated so that no personally identifiable information can be determined for the user, or a user&#39;s geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over how information is collected about the user and used by the system. 
     With respect to any or all of the message flow diagrams, scenarios, and flow charts in the figures and as discussed herein, each step, block and/or communication may represent a processing of information and/or a transmission of information in accordance with example embodiments. Alternative embodiments are included within the scope of these example embodiments. In these alternative embodiments, for example, functions described as steps, blocks, transmissions, communications, requests, responses, and/or messages may be executed out of order from that shown or discussed, including in substantially concurrent or in reverse order, depending on the functionality involved. Further, more or fewer steps, blocks and/or functions may be used with any of the message flow diagrams, scenarios, and flow charts discussed herein, and these message flow diagrams, scenarios, and flow charts may be combined with one another, in part or in whole. 
     A step or block that represents a processing of information may correspond to circuitry that can be configured to perform the specific logical functions of a herein-described method or technique. Alternatively or additionally, a step or block that represents a processing of information may correspond to a module, a segment, or a portion of program code (including related data). The program code may include one or more instructions executable by a processor for implementing specific logical functions or actions in the method or technique. The program code and/or related data may be stored on any type of computer-readable medium, such as a storage device, including a disk drive, a hard drive, or other storage media. 
     The computer-readable medium may also include non-transitory computer-readable storage media such as computer-readable media that stores data for short periods of time like register memory, processor cache, and/or random access memory (RAM). The computer-readable media may also include non-transitory computer-readable storage media that stores program code and/or data for longer periods of time, such as secondary or persistent long term storage, like read only memory (ROM), optical or magnetic disks, and/or compact-disc read only memory (CD-ROM), for example. The computer-readable media may also be any other volatile or non-volatile storage systems. A computer-readable medium may be considered a computer-readable storage medium, for example, or a tangible storage device. 
     Moreover, a step or block that represents one or more information transmissions may correspond to information transmissions between software and/or hardware modules in the same physical device. However, other information transmissions may be between software modules and/or hardware modules in different physical devices. 
     While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.