Patent Publication Number: US-2017364516-A1

Title: Linguistic model selection for adaptive automatic speech recognition

Description:
TECHNICAL FIELD 
     This disclosure pertains to dynamically selecting a linguistic model for automatic speech recognition, and more particularly, to dynamically selecting acoustic and language models for adaptive automatic speech recognition using biometric information. 
     BACKGROUND 
     Automatic speech recognition (ASR) systems help natural language interfaces recognize human speech and turn it into text that can be processed further. ASR systems rely on linguistic models (e.g., acoustic models, language models, phonetic dictionaries, etc.) to achieve this. Current ASR systems use specific linguistic models that are not adaptive to the user or the environment in which the input is fed into the system. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic block diagram of a system that includes an adaptive automatic speech recognition system in accordance with embodiments of the present disclosure. 
         FIG. 2  is a schematic block diagram of an adaptive automatic speech recognition system in accordance with embodiments of the present disclosure. 
         FIG. 3  is a schematic block diagram of a dialog system that uses an adaptive automatic speech recognition system in accordance with embodiments of the present disclosure. 
         FIG. 4  is a process flow diagram for selecting a linguistic model for automatic speech recognition in accordance with embodiments of the present disclosure. 
         FIG. 5  is a process flow diagram for selecting a linguistic model for automatic speech recognition based on a heartrate input in accordance with embodiments of the present disclosure. 
         FIG. 6  is a process flow diagram for selecting a parser model and intent classifier model in accordance with embodiments of the present disclosure. 
         FIG. 7  is an example illustration of a processor according to an embodiment of the present disclosure. 
         FIG. 8  is a schematic block diagram of a mobile device in accordance with embodiments of the present disclosure. 
         FIG. 9  is a schematic block diagram of a computing system according to an embodiment of the present disclosure. 
         FIG. 10  is a process flow diagram for training an acoustic model for biometric input-based speech recognition. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure describes an adaptive automatic speech recognition (ASR) system that dynamically changes linguistic models for ASR based on input from biometric sensors, as well as other contextual cues. Example contextual cues include user data (demographic, gender, acoustic properties of the voice such as pitch range), environmental factors (noise level, GPS location), communication success as measured based on dialog system performance/user experience given certain models). The use of targeted linguistic libraries results in a more accurate ASR experience. For example, exhaustion is known to modulate a speaker&#39;s voice, and a linguistic model trained on only exhausted speech may do better for an exhausted user than a more generic linguistic model. 
     This disclosure describes using the specific acoustic input received, preceding discourse, user exhaustion, the current state of the application, and input from biometric sensors to learn the specific circumstances under which the application is used. Sensors can, for example, note background noise to go to a more interference robust set of linguistic models. Biometric sensors, such as heart rate monitors, may cause the application to switch between at least two linguistic models, for example, such as one trained on fatigued and another trained on rested voices. Based on that, the system may process user input in different ways (e.g., switch to different automatic speech recognition models, dialog rules and classifiers, syntactic parsers or other natural language understanding tools). Examples include (1) allowing for more pauses between words, or, if an utterance isn&#39;t recognized, wait for more speech and combine the result with the previous utterance and try again; (2) switching to a different “tired voice” ASR model if the biometric data suggests that that might be needed; and (3) switching to a different parser, allowing for sloppier (more phonologically reduced) English when the user is tired and leaves out words (“what heart-rate” instead of “what is my heart-rate”) or uses ungrammatical utterances (“How drive to Palo Alto” instead of “How do I drive to Palo Alto”). 
       FIG. 1  is a schematic block diagram of a system  100  that includes an adaptive automatic speech recognition system in accordance with embodiments of the present disclosure. The system  100  includes an adaptive automatic speech recognition (AASR) module  102  that can be implemented in hardware, software, or a combination of hardware and software. The AASR module  102  can be communicably coupled to and receive input from a sound input  112  and a biometric input  110 . The AASR module  102  can output recognized text to a dialog system  104 . 
     Generally speaking, the dialog system  104  can receive textual inputs from the AASR module  102  to interpret the speech input and provide an appropriate response, in the form of an executed command, a verbal response (oral or textual), or some combination of the two. The system  100  also includes a processor  106  for executing instructions from the dialog system  104 . The system  100  can also include a speech synthesizer  124  that can synthesize a voice output from the textual speech. And an auditory output  126  that outputs audible sounds, including synthesized voice sounds, via speaker or headphones or Bluetooth connected device, etc. The system  100  also includes a display  128  that can display textual information as part of a dialog, as a response to an instruction or inquiry, or for other reasons. 
     In some embodiments, system  100  also includes a GPS system  114  configured to provide location information to system  100 . In some embodiments, the GPS system  114  can input location information into the dialog system  104  so that the dialog system  104  can use the location information for contextual interpretation of speech text received from the AASR module  102 . 
     System  100  can include a memory  108 . Memory  108  can include a hard drive, solid state drive, flash memory, or other type of storage unit or device. Memory  108  can store data, such as biometric database  116  and linguistic library  118 . Biometric database  116  can store personalized biometric information that the AASR module  102  can use as a baseline or as a threshold to compare against biometric signals received from the biometric sensor  111 . Based on the comparison between received biometric signals and the baseline or threshold biometric information stored in biometric database  116 , the AASR module can select a linguistic model appropriate for the context derived from the biometric comparison. The linguistic model can include one or both of an acoustic model  120  or a language model  122 , both of which can be stored in the linguistic library  118 . 
     In some embodiments, the AASR can use machine learning and/or neural networks to be trained and to learn how to select a linguistic model. 
     In general terms, an acoustic model can model a relationship between a received audio signal and phonetic units in the language. A language model is responsible for modeling the phonetic unit sequences in the language. 
     The biometric sensor  111  can include any type of sensor that can receive a biometric signal from a user and convert that signal into an electronic signal. An example of a biometric sensor  111  includes a heartbeat sensor. Another example is a pulse oximeter, EEG, sweat sensor, breath rate sensor, pedometer, etc. In some embodiments, the biometric sensor  111  can include an inertial sensor to detect vibrations of the user, such as whether the users hands are shaking, etc. The biometric sensor  111  can convert biometric signals into corresponding electrical signals and input the biometric electrical signals to the AASR module  102  via a biometric input. 
     Other examples of biometric information can include heart rate, stride rate, cadence, breath rate, vocal fry, breathy phonation, amount of sweat, EEG data, etc. 
     The system  100  can also include a microphone  113  for converting audible sound into corresponding electrical sound signals. The sound signals are provided to the AASR module  102  via a sound signal input  112 . 
       FIG. 2  is a schematic block diagram  200  of an adaptive automatic speech recognition (AASR) system  102  in accordance with embodiments of the present disclosure. The AASR system  102  can be a stand-alone device, a part of a wearable unit, or part of a larger system. The AASR system  102  can be implemented hardware, software, or a combination of hardware and software. 
     The AASR system  102  can include an adaptive automatic speech recognition module  102  implemented in hardware, software, or a combination of hardware and software. The AASR module  200  can include a biometric signal processor  202  and a speech recognition module  204 . The biometric signal processor  202  can receive an electrical signal representing a biometric signal from a biometric input  110  (which is communicably coupled to a biometric sensor, as shown in  FIG. 1 ). 
     The biometric signal processor  202  can process the biometric input  110  to identify a linguistic model that compensates for a potential change in the speaker&#39;s speech patterns, tones, syntax, distortion, diction, etc. that may occur when the speaker&#39;s biometric parameters is different form the normal or baseline biometric values associated with that user or with the population in general. For example, a heightened heartrate may cause the biometric signal processor  202  to select a linguistic model that compensates for changes in speech patterns associated with heightened heartrates. Such speech patterns include increased breathy phonation, exaggerated phonetic lengthening, more frequent pauses, more pauses within constituents (in unlikely linguistic contexts), strong breathing, frequent breathing noises, etc. 
     The biometric signal processor  202  can access a biometric information database  116  (or biometric database  116  for short). The biometric database  116  can store biometric information  210 . Biometric information  210  can include user-defined biometric norms or thresholds or baselines that can be used by the biometric signal processor  202  to determine how to select a linguistic model. For example, a user can program the biometric database  116  with biometric information  210  such as resting heartrate, normal pulse-ox value, etc. The biometric signal processor  202  can receive a biometric signal from a biometric input  110 . The biometric signal processor  202  can compare the biometric signal with corresponding biometric information  210  stored in the biometric database  116 . The biometric signal processor  202  can then select a linguistic model based on the comparison between the received biometric signal and the stored biometric information. 
     Specifically, the biometric signal processor  202  can access a linguistic library  118 . Linguistic library  118  can store a plurality of acoustic models, such as acoustic model  1   222 , acoustic model  2   224 , . . . acoustic model M  226 , etc. The biometric signal processor  202  can select from among the various acoustic models depending on the biometric input signal received. Similarly, the linguistic library  118  can store a plurality of language models, such as language model  1   222 , language model  2   224 , . . . language model N  226 , etc. The biometric signal processor  202  can select from among the various language models depending on the biometric input signal received, and in some cases, the biometric signal processor  202  can filter the language models based on a selected acoustic mode (and vice versa). 
     The AASR system  102  can include a speech recognition module  204  for converting received speech input signals into a computer-readable format, such as a textual format. The AASR system  102  can also include a speech input  112  (which is communicably coupled to a microphone or other audio input device). The speech recognition module  204  can receive an electrical signal representing speech from speech input  112 . The speech recognition module  204  can use the selected linguistic model (i.e., the selected acoustic model and selected language model) from the biometric signal processor  202  to process the received speech signal to convert the speech signal into the computer readable format. 
       FIG. 3  is a schematic block diagram  300  of a dialog system  104  that uses an adaptive automatic speech recognition (AASR) system  102  in accordance with embodiments of the present disclosure. The AASR system  102  can provide a processed speech signal to the dialog system  104  in the form of a computer readable format, such as a text format. The dialog system  104  can process the received textual speech signal to determine the intent of the speaker and to engage in a conversation with the user to clarify the user&#39;s intent if the dialog system cannot determine the intent of the user. Additionally, the dialog system can provide feedback to the user based on a determined intent, such as verbally (i.e., orally, textually, etc.) answering a request or answering a question. 
     The dialog system  104  can include a parser module  302  implemented in hardware, software, or a combination of hardware and software. Parser module  302  is configured to receive the textual speech signal from the AASR system  102 . The parser module  302  is configured to assemble a cohesive set of words, such as a sentence, sentence fragment, etc. from the received textual speech signal. The parser module  302  can then provide the cohesive set of words to an intent classifier module  304  implemented in hardware, software, or a combination. The intent classifier module  304  can determine an intent of the speaker. The intent classifier  304  can access a dialog database  316  stored in memory  310 . The dialog database  304  can store relational information that connects a cohesive set of words to an instruction (e.g., instruction that causes a device to do something) or a response (e.g., answer to a question) or both (e.g., execute an instruction and provide a response). The dialog system  104  can then output an instruction to the processor  106  that can execute the instruction. The processor  106  can also provide an input back to the dialog system  104 , which can use the input to configure a confirmation message or response, based on the determined intent of the speaker. The dialog system  104  can also output a signal to a speech synthesizer  124  that synthesizes an audible voice to provide the speaker (and others in ear-shot of the speaker) a response to the user&#39;s speech signal. 
     In some embodiments, the dialog system  104  can select a parser model from a plurality of parser models  312  stored in memory  310 . The dialog system  104  can select the parser model based on the selected acoustic model  320  and/or the selected language model  322 . Similarly, the dialog system  104  can select an intent classifier model from a plurality of intent classifier models  314  stored in memory  310  based on the selected acoustic model  320  and/or the selected language model  322 . 
       FIG. 4  is a process flow diagram  400  for selecting a linguistic model for automatic speech recognition (ASR) in accordance with embodiments of the present disclosure. An adaptive ASR system can receive an audible speech signal (i.e., an electrical signal representative of an audible speech signal) ( 402 ). The adaptive ASR system can also receive a biometric signal ( 404 ). The adaptive ASR system can determine an acoustic model based on the biometric signal ( 406 ). The adaptive ASR system can determine a language model based on the biometric signal ( 408 ). In some implementations, the language model can be determined based on both the biometric signal and the selected acoustic model. In some implementations, the language model can be determined based on the selected acoustic model. The adaptive ASR system can process the audible speech signal for speech recognition using the identified acoustic model and identified language model. 
       FIG. 5  is a process flow diagram  500  for selecting a linguistic model for automatic speech recognition based on a heartrate input in accordance with embodiments of the present disclosure.  FIG. 5  provides one example implementation for selecting a linguistic model for speech recognition based on a biometric signal—in this case a heartrate. The adaptive ASR system can receive a heartrate signal ( 502 ) from, e.g., a heartrate monitor. The adaptive ASR system can compare the received heartrate signal with a threshold value ( 504 ). The threshold value can be defined by the user by, e.g., by entering into the system a resting heartrate. The threshold value can also be identified based on an average resting heartrate for people in the user&#39;s age group, weight, height, etc. Multiple threshold values can also be used. For example, a first threshold can represent a resting heartrate, which is associated with a first linguistic model. A second threshold value can represent a heartrate associated with a second linguistic model. Table 1 provides an example relational table for associating heartrate values with linguistic models: 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Heartrate 
                 Acoustic Model 
                 Language Model 
               
               
                   
                   
               
             
            
               
                   
                 H ≦ H1 
                 Acoustic Model X 
                 Language Model A 
               
               
                   
                 H2 ≧ H &gt; H1 
                 Acoustic Model Y 
                 Language Model B 
               
               
                   
                 H ≧ H2 
                 Acoustic Model Z 
                 Language Model C 
               
               
                   
                   
               
            
           
         
       
     
     For a heartrate less than or equal to a first threshold value, the adaptive ASR system can use a first linguistic model (such as a standard linguistic model) ( 512 ). For a heartrate greater than a threshold, the adaptive ASR system can identify an acoustic model associated with that heartrate ( 506 ). For a heartrate greater than a threshold, the adaptive ASR system can identify a language model associated with that heartrate ( 508 ). In some cases, the language model can be based on the selected acoustic model or both the heartrate and the acoustic model. The adaptive ASR system can process an audible speech signal for speech recognition using the identified acoustic model and identified language model. 
       FIG. 6  is a process flow diagram  600  for selecting a parser model and intent classifier model in accordance with embodiments of the present disclosure. The dialog system can receive an identification of a linguistic model from an adaptive ASR system ( 602 ). The dialog system can identify a parser model based on the identified linguistic model ( 604 ). The dialog system can identify an intent classifier based on the identified linguistic model or both the identified linguistic model and the identified parser model. The dialog system can process an audible speech signal for dialog using the identified parser model and the identified intent classifier model. 
       FIGS. 7-9  are block diagrams of exemplary computer architectures that may be used in accordance with embodiments disclosed herein. Other computer architecture designs known in the art for processors, mobile devices, and computing systems may also be used. Generally, suitable computer architectures for embodiments disclosed herein can include, but are not limited to, configurations illustrated in  FIGS. 7-9 . 
       FIG. 7  is an example illustration of a processor according to an embodiment. Processor  700  is an example of a type of hardware device that can be used in connection with the implementations above. 
     Processor  700  may be any type of processor, such as a microprocessor, an embedded processor, a digital signal processor (DSP), a network processor, a multi-core processor, a single core processor, or other device to execute code. Although only one processor  700  is illustrated in  FIG. 7 , a processing element may alternatively include more than one of processor  700  illustrated in  FIG. 7 . Processor  700  may be a single-threaded core or, for at least one embodiment, the processor  700  may be multi-threaded in that it may include more than one hardware thread context (or “logical processor”) per core. 
       FIG. 7  also illustrates a memory  702  coupled to processor  700  in accordance with an embodiment. Memory  702  may be any of a wide variety of memories (including various layers of memory hierarchy) as are known or otherwise available to those of skill in the art. Such memory elements can include, but are not limited to, random access memory (RAM), read only memory (ROM), logic blocks of a field programmable gate array (FPGA), erasable programmable read only memory (EPROM), and electrically erasable programmable ROM (EEPROM). 
     Processor  700  can execute any type of instructions associated with algorithms, processes, or operations detailed herein. Generally, processor  700  can transform an element or an article (e.g., data) from one state or thing to another state or thing. 
     Code  704 , which may be one or more instructions to be executed by processor  700 , may be stored in memory  702 , or may be stored in software, hardware, firmware, or any suitable combination thereof, or in any other internal or external component, device, element, or object where appropriate and based on particular needs. In one example, processor  700  can follow a program sequence of instructions indicated by code  704 . Each instruction enters a front-end logic  706  and is processed by one or more decoders  708 . The decoder may generate, as its output, a micro operation such as a fixed width micro operation in a predefined format, or may generate other instructions, microinstructions, or control signals that reflect the original code instruction. Front-end logic  706  also includes register renaming logic  710  and scheduling logic  712 , which generally allocate resources and queue the operation corresponding to the instruction for execution. 
     Processor  700  can also include execution logic  714  having a set of execution units  716   a ,  716   b ,  716   n , etc. Some embodiments may include a number of execution units dedicated to specific functions or sets of functions. Other embodiments may include only one execution unit or one execution unit that can perform a particular function. Execution logic  714  performs the operations specified by code instructions. 
     After completion of execution of the operations specified by the code instructions, back-end logic  718  can retire the instructions of code  704 . In one embodiment, processor  700  allows out of order execution but requires in order retirement of instructions. Retirement logic  720  may take a variety of known forms (e.g., re-order buffers or the like). In this manner, processor  700  is transformed during execution of code  704 , at least in terms of the output generated by the decoder, hardware registers and tables utilized by register renaming logic  710 , and any registers (not shown) modified by execution logic  714 . 
     Although not shown in  FIG. 7 , a processing element may include other elements on a chip with processor  700 . For example, a processing element may include memory control logic along with processor  700 . The processing element may include I/O control logic and/or may include I/O control logic integrated with memory control logic. The processing element may also include one or more caches. In some embodiments, non-volatile memory (such as flash memory or fuses) may also be included on the chip with processor  700 . 
     Referring now to  FIG. 8 , a block diagram is illustrated of an example mobile device  800 . Mobile device  800  is an example of a possible computing system (e.g., a host or endpoint device) of the examples and implementations described herein. In an embodiment, mobile device  800  operates as a transmitter and a receiver of wireless communications signals. Specifically, in one example, mobile device  800  may be capable of both transmitting and receiving cellular network voice and data mobile services. Mobile services include such functionality as full Internet access, downloadable and streaming video content, as well as voice telephone communications. 
     Mobile device  800  may correspond to a conventional wireless or cellular portable telephone, such as a handset that is capable of receiving “3G”, or “third generation” cellular services. In another example, mobile device  800  may be capable of transmitting and receiving “4G” mobile services as well, or any other mobile service. 
     Examples of devices that can correspond to mobile device  800  include cellular telephone handsets and smartphones, such as those capable of Internet access, email, and instant messaging communications, and portable video receiving and display devices, along with the capability of supporting telephone services. It is contemplated that those skilled in the art having reference to this specification will readily comprehend the nature of modern smartphones and telephone handset devices and systems suitable for implementation of the different aspects of this disclosure as described herein. As such, the architecture of mobile device  800  illustrated in  FIG. 8  is presented at a relatively high level. Nevertheless, it is contemplated that modifications and alternatives to this architecture may be made and will be apparent to the reader, such modifications and alternatives contemplated to be within the scope of this description. 
     In an aspect of this disclosure, mobile device  800  includes a transceiver  802 , which is connected to and in communication with an antenna. Transceiver  802  may be a radio frequency transceiver. Also, wireless signals may be transmitted and received via transceiver  802 . Transceiver  802  may be constructed, for example, to include analog and digital radio frequency (RF) ‘front end’ functionality, circuitry for converting RF signals to a baseband frequency, via an intermediate frequency (IF) if desired, analog and digital filtering, and other conventional circuitry useful for carrying out wireless communications over modern cellular frequencies, for example, those suited for 3G or 4G communications. Transceiver  802  is connected to a processor  804 , which may perform the bulk of the digital signal processing of signals to be communicated and signals received, at the baseband frequency. Processor  804  can provide a graphics interface to a display element  808 , for the display of text, graphics, and video to a user, as well as an input element  510  for accepting inputs from users, such as a touchpad, keypad, roller mouse, and other examples. Processor  804  may include an embodiment such as shown and described with reference to processor  700  of  FIG. 7 . 
     In an aspect of this disclosure, processor  804  may be a processor that can execute any type of instructions to achieve the functionality and operations as detailed herein. Processor  804  may also be coupled to a memory element  806  for storing information and data used in operations performed using the processor  804 . Additional details of an example processor  804  and memory element  806  are subsequently described herein. In an example embodiment, mobile device  800  may be designed with a system-on-a-chip (SoC) architecture, which integrates many or all components of the mobile device into a single chip, in at least some embodiments. 
       FIG. 9  is a schematic block diagram of a computing system  900  according to an embodiment. In particular,  FIG. 9  shows a system where processors, memory, and input/output devices are interconnected by a number of point-to-point interfaces. Generally, one or more of the computing systems described herein may be configured in the same or similar manner as computing system  900 . 
     Processors  970  and  980  may also each include integrated memory controller logic (MC)  972  and  982  to communicate with memory elements  932  and  934 . In alternative embodiments, memory controller logic  972  and  982  may be discrete logic separate from processors  970  and  980 . Memory elements  932  and/or  934  may store various data to be used by processors  970  and  980  in achieving operations and functionality outlined herein. 
     Processors  970  and  980  may be any type of processor, such as those discussed in connection with other figures. Processors  970  and  980  may exchange data via a point-to-point (PtP) interface  950  using point-to-point interface circuits  978  and  988 , respectively. Processors  970  and  980  may each exchange data with a chipset  990  via individual point-to-point interfaces  952  and  954  using point-to-point interface circuits  976 ,  986 ,  994 , and  998 . Chipset  990  may also exchange data with a high-performance graphics circuit  938  via a high-performance graphics interface  939 , using an interface circuit  992 , which could be a PtP interface circuit. In alternative embodiments, any or all of the PtP links illustrated in  FIG. 9  could be implemented as a multi-drop bus rather than a PtP link. 
     Chipset  990  may be in communication with a bus  920  via an interface circuit  996 . Bus  920  may have one or more devices that communicate over it, such as a bus bridge  918  and I/O devices  916 . Via a bus  910 , bus bridge  918  may be in communication with other devices such as a keyboard/mouse  912  (or other input devices such as a touch screen, trackball, etc.), communication devices  926  (such as modems, network interface devices, or other types of communication devices that may communicate through a computer network  960 ), audio I/O devices  914 , and/or a data storage device  928 . Data storage device  928  may store code  930 , which may be executed by processors  970  and/or  980 . In alternative embodiments, any portions of the bus architectures could be implemented with one or more PtP links. 
     The computer system depicted in  FIG. 9  is a schematic illustration of an embodiment of a computing system that may be utilized to implement various embodiments discussed herein. It will be appreciated that various components of the system depicted in  FIG. 9  may be combined in a system-on-a-chip (SoC) architecture or in any other suitable configuration capable of achieving the functionality and features of examples and implementations provided herein. 
       FIG. 10  is a process flow diagram  1000  for training an acoustic model for biometric input-based speech recognition. An adaptive ASR system training module can be provided a first set of speech patterns ( 1002 ). The training module can be provided a first set of biometric information ( 1004 ). The training module can train the a first acoustic model based on the first set of speech patterns and the first set of biometric information ( 1006 ). The training module can associate the first acoustic model with the first set of biometric information ( 1008 ). The adaptive ASR system training module can be provided a first set of speech patterns ( 1010 ). The training module can be provided a second set of biometric information ( 1012 ). The training module can train the a second acoustic model based on the second set of speech patterns and the second set of biometric information ( 1014 ). The training module can associate the second acoustic model with the second set of biometric information ( 1016 ). 
     Although this disclosure has been described in terms of certain implementations and generally associated methods, alterations and permutations of these implementations and methods will be apparent to those skilled in the art. For example, the actions described herein can be performed in a different order than as described and still achieve the desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing may be advantageous. Additionally, other user interface layouts and functionality can be supported. Other variations are within the scope of the following claims. 
     Example 1 is an adaptive automatic speech recognition (ASR) device that includes a sound input to receive a speech input; a biometric input to receive a biometric signal; and an biometric processor in communication with the biometric input. The biometric processor to receive the biometric signal; identify a linguistic model based on the biometric signal; and a speech recognition modules to process the speech input for speech recognition using the identified linguistic model. 
     Example 2 may include the subject matter of example 1, wherein the linguistic model comprises one or both of an acoustic model or a language model. 
     Example 3 may include the subject matter of example 1 or 2, wherein the biometric signal comprises a signal representing a heartbeat. 
     Example 4 may include the subject matter of example 1 or 2 or 3, further comprising a biometric sensor in communication with the biometric input. 
     Example 5 may include the subject matter of example 1 or 2 or 3 or 4, further comprising a microphone in communication with the sound input. 
     Example 6 may include the subject matter of example 1 or 2 or 3 or 4 or 5, further comprising a biometric database to store biometric information associated with a user of the adaptive ASR device; and wherein the biometric processor is configured to compare the received biometric signal with a biometric information stored in the biometric database; and select the linguistic model based on the comparison of the received biometric signal and the stored biometric information. 
     Example 7 may include the subject matter of example 1 or 2 or 3 or 4 or 5 or 6, wherein the biometric signal indicates a context of the speech input and wherein the selected linguistic model compensates for the context of the speech input. 
     Example 8 may include the subject matter of example 1 or 2 or 3 or 4 or 5 or 6 or 7, further comprising a linguistic library, the linguistic library comprising a plurality of acoustic models, each acoustic model of the plurality of acoustic model associated with a biometric context. 
     Example 9 may include the subject matter of example 8, wherein the linguistic library comprises a plurality of language models, each language model of the plurality of language models associated with a biometric context. 
     Example 10 may include the subject matter of example 8 or 9, wherein the biometric context is based on a biometric input. 
     Example 11 is a method comprising receiving a speech signal; receiving a biometric signal from a biometric sensor implemented at least partially in hardware; determining a linguistic model based on the biometric signal; and processing the speech signal for speech recognition using the linguistic model based on the biometric signal. 
     Example 12 may include the subject matter of example 11, wherein the linguistic model comprises one or both of an acoustic model or a language model. 
     Example 13 may include the subject matter of example 11 or 12, further comprising comparing the received biometric signal with biometric information stored in the biometric database; and selecting the linguistic model based on the comparison of the received biometric signal and the stored biometric information. 
     Example 14 may include the subject matter of example 11 or 12 or 13, further comprising a selecting the linguistic model from a linguistic library, the linguistic library comprising a plurality of acoustic models, each acoustic model of the plurality of acoustic model associated with a biometric context, and a plurality of language models, each language model of the plurality of language models associated with a biometric context. 
     Example 15 is a system comprising an adaptive automatic speech recognition device comprising a sound input to receive a speech input; a biometric input to receive a biometric signal; an biometric processor in communication with the biometric input to receive the biometric signal; identify a linguistic model based on the biometric signal; and a speech recognition modules to process the speech input for speech recognition using the identified linguistic model. The system also includes a a dialog system comprising a parser module to convert the recognized speech into an instruction; and an intent classifier module to determine a command to execute on the system based on the instruction. 
     Example 16 may include the subject matter of example 15, wherein the linguistic model comprises one or both of an acoustic model or a language model. 
     Example 17 may include the subject matter of example 15 or 16, wherein the biometric signal comprises a signal representing a heartbeat. 
     Example 18 may include the subject matter of example 15 or 16 or 17, further comprising a biometric sensor in communication with the biometric input. 
     Example 19 may include the subject matter of example 15 or 16 or 17 or 18, further comprising a microphone in communication with the sound input. 
     Example 20 may include the subject matter of example 15 or 16 or 17 or 18 or 19, further comprising a biometric database to store biometric information associated with a user of the adaptive ASR device; and wherein the biometric processor is configured to compare the received biometric signal with a biometric information stored in the biometric database; and select the linguistic model based on the comparison of the received biometric signal and the stored biometric information. 
     Example 21 may include the subject matter of example 15 or 16 or 17 or 18 or 19 or 20, wherein the biometric signal indicates a context of the speech input and wherein the selected linguistic model compensates for the context of the speech input. 
     Example 22 may include the subject matter of example 15 or 16 or 17 or 18 or 19 or 20 or 21, further comprising a linguistic library, the linguistic library comprising a plurality of acoustic models, each acoustic model of the plurality of acoustic model associated with a biometric context. 
     Example 23 may include the subject matter of example 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22, wherein the linguistic library comprises a plurality of language models, each language model of the plurality of language models associated with a biometric context. 
     Example 24 may include the subject matter of example 22 or 23, wherein the biometric context is based on a biometric input. 
     Example 25 may include the subject matter of example 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24, wherein the dialog system is configured to select one or both of a parser module or an intent classifier module based on the biometric input. 
     While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any disclosures or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular disclosures. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results.