Patent Publication Number: US-10311878-B2

Title: Incorporating an exogenous large-vocabulary model into rule-based speech recognition

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a divisional application of co-pending U.S. patent application Ser. No. 14/158,147, entitled “INCORPORATING AN EXOGENOUS LARGE-VOCABULARY MODEL INTO RULE-BASED SPEECH RECOGNITION”, filed on Jan. 17, 2014, the entire disclosure of which is hereby incorporated herein by reference. 
    
    
     BACKGROUND 
     Speech recognition systems can largely be classified into two types of systems: a rule-based system that utilizes a small-vocabulary and produces highly accurate results, or an open-ended, statistical-based system that utilizes a vast vocabulary and provides a breadth of recognition at a cost of specific accuracy. 
     Speech recognition has become a useful tool on smaller form devices, such as mobile phones, tablet computers, wearable devices (e.g., smart watches, etc.), portable media players, etc. Users may use a speech recognition system with various types of applications to perform actions, answer questions, make recommendations, etc. For example, a user may speak a command to launch a text messaging application, speak a text message, and then speak a command to send the text message. Speech recognition on such devices may be constrained by hardware, software, and/or processing/memory capabilities. Accordingly, smaller form devices may comprise a rule-based speech recognition system as opposed to a large-vocabulary model that allows for open-ended speech because of the amount of memory and processing power such a system may consume. 
     A large-vocabulary speech recognition system may be available on a separate system, for example, on a remote server. Some smaller form devices may rely on a network-based large-vocabulary speech recognition system to perform recognition; however, access to a network may not always be available, and hosting a large-vocabulary system on a smaller form device may not be feasible from a computational perspective. 
     Oftentimes, there exists a need to combine advantages of both types of speech recognition systems, for example, where a portion of a spoken utterance from a user may need to be matched with high accuracy, and another portion of the spoken utterance may need to be more inspecifically matched. It is with respect to these and other considerations that the present invention has been made. 
     SUMMARY 
     This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended as an aid in determining the scope of the claimed subject matter. 
     Embodiments of the present invention solve the above and other problems by providing an incorporation of an exogenous large-vocabulary model into rule-based speech recognition. A combination of constrained, rule-based recognition on a local device may be leveraged with remote open-ended statistically-based recognition to deliver recognition results that incorporate both the specificity of device-based rules and the breadth of a large language model. 
     The details of one or more embodiments are set forth in the accompanying drawings and description below. Other features and advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. It is to be understood that the following detailed description is explanatory only and is not restrictive of the invention as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present invention. In the drawings: 
         FIG. 1  is a block diagram of one embodiment of a system for providing an incorporation of an exogenous large-vocabulary model into rule-based speech recognition; 
         FIG. 2  is a block diagram illustrating a request and response transaction between a small-vocabulary speech recognition system and a large-vocabulary speech recognition system; 
         FIGS. 3A-3C  illustrate a flow chart of a method for providing an incorporation of an exogenous large-vocabulary model into rule-based speech recognition; 
         FIG. 4  is a block diagram illustrating example physical components of a computing device with which embodiments of the invention may be practiced; 
         FIGS. 5A and 5B  are simplified block diagrams of a mobile computing device with which embodiments of the present invention may be practiced; and 
         FIG. 6  is a simplified block diagram of a distributed computing system in which embodiments of the present invention may be practiced. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present invention are directed to providing an incorporation of an exogenous large-vocabulary model into rule-based speech recognition. A user of a device (e.g., a mobile phone, a tablet device, etc.) may provide an audio stream to the device. For example, the user may speak a command and/or dictate a message. Recognition of the audio stream may be performed by a small-vocabulary rule-based speech recognition system on the local device. According to an embodiment, the audio stream may also be streamed to a remote large-vocabulary speech recognition system for recognition using a statistical language model. The large-vocabulary speech recognition system recognition may occur concurrently with the small-vocabulary rule-based speech recognition system recognition. 
     The small-vocabulary rule-based speech recognition system may comprise a hybrid recognition rule that may be triggered when a portion of the audio is not recognized by the small-vocabulary rule-based speech recognition system. A mark-up indicative of an unrecognized portion(s) may be inserted into the recognition result. The recognition result including any mark-ups may be sent to the large-vocabulary speech recognition system. Upon detection of a mark-up, recognition of a portion of the audio specified by the mark-up may be performed using the statistical language model. The result of the large-vocabulary speech recognition system may be unified with the small-vocabulary rule-based speech recognition system result and sent as a hybrid recognition response back to the small-vocabulary speech recognition system. 
     Embodiments may also comprise an arbitration algorithm pre-trained to reduce word error rates of recognitions. The arbitration algorithm may be operable to determine whether to use the small-vocabulary speech recognition system recognition result, the large-vocabulary speech recognition system recognition result, or, if the hybrid recognition rule is triggered, the hybrid recognition result. According to an embodiment, the arbitration algorithm may be network-based and may be evoked prior to sending a recognition response to the small-vocabulary speech recognition system. 
     The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawing and the following description to refer to the same or similar elements. While embodiments of the invention may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the invention, but instead, the proper scope of the invention is defined by the appended claims. 
     Referring now to the drawings, in which like numerals represent like elements, various embodiments will be described.  FIG. 1  is a block diagram illustrating a system architecture  100  for providing an incorporation of an exogenous large-vocabulary model into rule-based speech recognition. The system  100  includes a device  110  comprising a small-vocabulary speech recognition system  115 . The device  110  may be one of a variety of suitable computing devices described below with reference to  FIGS. 4 through 6 . For example, the device  110  may include a tablet computing device, a desktop computer, a mobile communication device, a laptop computer, a laptop/tablet hybrid computing device, a gaming device, a media playing device, or other type of computing device for executing applications for performing a variety of tasks. 
     According to embodiments, the small-vocabulary speech recognition system (SVSRS)  115  may include a recognizer  125  and rule-based grammars  120 . According to embodiments, when a user  105  speaks an utterance to the device  110 , the audio  160  may be received by the SVSRS  115  for rule-based speech recognition. Recognition of the audio may be performed by the SVSRS  115  using the recognition rules (rule-based grammars  120 ) authored on the device  110 . According to an embodiment, the rule-based grammars  120  may be authored using an XML format, for example, in a speech recognition grammar specification (SRGS) format. The rule-based grammars  120  may include a hybrid recognition rule  122 . 
     According to an embodiment, the audio  160  may be simultaneously streamed to a large vocabulary speech recognition system (LVSRS)  145 . The LVSRS  145  may include a recognizer  155  and an open-ended statistical language model  150  operable to perform open-ended statistically-based recognition. According to one embodiment, the LVSRS  145  may be locally-based. According to another embodiment and as illustrated in  FIG. 1 , the LVSRS  145  may be cloud-hosted and provided as a service. For example, the LVSRS  145  may be communicatively connected to a server device  130  (or a plurality of server devices) by way of a network  140 , such as an intranet, extranet, or the Internet. 
     With reference now to  FIG. 2 , a block diagram of a request and response transaction  200  between the SVSRS  115  and the LVSRS  145  is illustrated. As described above, an utterance or audio  160  may be received by the SVSRS  115 , and may be streamed to the LVSRS  145  for simultaneous recognition. As the SVSRS  115  is performing its rule-based recognition, the sequence of rules may be matched by the recognition system. The SVSRS rule-matched recognition results  215  may be inserted into a recognition result  210 , serialized, and sent as a request  230  to the LVSRS  145 . 
     Consider for example that a user  105  utters a statement such as, “call Bob.” The utterance (audio  160 ) may be easily recognized by the rule-based grammars of the SVSRS  115 . Accordingly, the SVSRS recognition result  210  may comprise a transcription of the utterance “call Bob” (SVSRS rule-matched recognition result  215 ). 
     If a portion of the audio is not matched by a rule, the hybrid recognition rule  122  may be triggered, and a place holder, herein referred to as a hybrid recognition rule mark-up  220 , may be inserted into the recognition result  210 . The hybrid recognition rule  122  may be triggered multiple times for a single audio stream  160 . Accordingly, a recognition result  210  may comprise multiple hybrid recognition rule mark-ups  220 A-N, as well as multiple SVSRS rule-matched recognition results  215 A-N. 
     For example, if a user utters a statement such as, “text Bob I may be running a little late, and remind me to buy some cookies.” Portions of the utterance may be recognized by the rule-based grammars of the SVSRS  115 . For example, the SVSRS  115  may be able to recognize “text Bob” and “remind me to;” however, the other portions of the audio  160  (“I may be running a little late, and” and “buy some cookies”) may not be recognizable by the SVSRS  115 . Accordingly, “text Bob” may be recognized and provided as a first SVSRS rule-matched recognition result  215 A in the SVSRS recognition result  210 . “I may be running a little late” may not be recognized, and thus may trigger the hybrid recognition rule  122 . Consequently, a first hybrid recognition rule mark-up  220 A may be inserted into the SVSRS recognition result  210 . “Remind me to” may be recognized and provided as a second SVSRS rule-matched recognition result  215 B in the SVSRS recognition result  210 . “Buy some cookies” may not be recognized, and again, the hybrid recognition rule  122  may be triggered and a second hybrid recognition rule mark-up  220 B may be inserted into the SVSRS recognition result  210 . 
     The SVSRS recognition result  210  including any SVSRS rule-matched recognition results  215  and any hybrid recognition rule mark-ups  220  may be structured and serialized as part of a request  230  to the LVSRS  145 . The request  230  may also comprise the audio  160  and metadata  225 , for example, client context, connection information, etc. According to an embodiment, the SVSRS recognition result  210  may be sent as a complete recognition result (including SVSRS rule-matched recognition results  215  and hybrid recognition rule mark-ups  220 ) after the SVSRS  115  has completed its local recognition. According to another embodiment, intermediate SVSRS recognition results may be serialized and sent as the user  105  is speaking. 
     According to an embodiment, a SVSRS recognition result  210  may comprise a confidence levels associated with rule-matched portions of an audio  160 . The below example is a sample recognition phrase (SVSRS recognition result  210 ) comprising confidence levels for an utterance, “text Sam that I need to run to the store after work.” 
     
       
         
           
               
             
               
                   
               
               
                 EXAMPLE: SVSRS RECOGNITION RESULT 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 { 
                   
               
               
                   
                   Text 
                 : “text Sam that ...”, 
               
               
                   
                   Confidence 
                 : “0.8712346”, 
               
               
                   
                   RuleName 
                 : “VoiceAgent_Text”, 
               
               
                   
                   Phrases 
                 : [ 
               
            
           
           
               
               
               
            
               
                   
                     { 
                   
               
               
                   
                       Text 
                 : “text”, 
               
               
                   
                       Rule 
                 : “VoiceAgent_Text”, 
               
               
                   
                       Confidence 
                 : “0.734724”, 
               
               
                   
                       }, 
               
               
                   
                       { 
               
               
                   
                       Text 
                 : “Sam”, 
               
               
                   
                       Rule 
                 : “Contact”, 
               
               
                   
                       Confidence 
                 : “0.921274”, 
               
               
                   
                     }, 
               
               
                   
                     { 
               
               
                   
                       Text 
                 : “that”, 
               
               
                   
                       Rule 
                 : “VoiceAgent_Text”, 
               
               
                   
                       Confidence 
                 : “0.734714”, 
               
               
                   
                     }, 
               
               
                   
                     { 
               
               
                   
                       Text 
                 : “...”, 
               
               
                   
                       Rule 
                 : “ShortMessageDictation”, 
               
               
                   
                       Confidence 
                 : “1.00000”, 
               
               
                   
                     } 
               
               
                   
                     ] 
               
               
                   
                 } 
               
               
                   
                   
               
            
           
         
       
     
     The request  230  may be received and deserialized by the LVSRS  145 . The LVSRS  145  may parse the SVSRS recognition result  210  and determine if it comprises any hybrid recognition rule mark-ups  220 . For example, a hybrid recognition rule markup  220  may be a mark-up such as, “ . . . ” as illustrated in the above example. If the SVSRS recognition result  210  comprises a hybrid recognition rule mark-up  220 , the LVSRS  145  may cancel its recognition that it had started simultaneously with the SVSRS recognition, and restart a recognition using the received SVSRS recognition result  210  as a template. The LVSRS  145  may perform open-ended statistically-based recognition on the portion(s) of the audio  160  specified by the hybrid recognition rule mark-up(s)  220 . 
     The result(s) of the LVSRS  145  (herein referred to as LVSRS recognition result(s)  235 ) may be pieced together with the SVSRS rule-matched recognition result(s)  215 . The combination of the LVSRS and the SVSRS recognition results (herein referred to as a hybrid recognition result)  240  may be serialized and sent as a response to the SVSRS  115 . 
       FIGS. 3A-3C  illustrates one embodiment of a method  300  for providing an incorporation of an exogenous large-vocabulary model into rule-based speech recognition.  FIGS. 3A and 3C  illustrate SVSRS  115  processes, and  FIG. 3B  illustrates LVSRS  145  processes. Referring now to  FIG. 3A , the method  300  starts at OPERATION  302  and proceeds to OPERATION  304  where audio  160  is received. For example, a user  105  may initiate a speech recognition feature on his/her device  110  to perform a task (e.g., to create a text message, perform an Internet search, transcribe notes into a notes application, etc.). The user  105  may speak an utterance, such as a command or a statement, into the device  110 . 
     The method  300  may proceed from OPERATION  304  to OPERATION  306  where recognition may be performed by the SVSRS  115  using the recognition rules (rule-based grammars  120 ) authored on the device  110 , and may also optionally proceed to OPERATION  305 , where the audio  160  is sent to the LVSRS  145  for recognition. For example, the audio  160  may be concurrently recognized by the SVSRS  115  and the LVSRS  145 . From OPERATION  305 , the method may proceed to OPERATION  313 , which will be described with reference to  FIG. 3B . 
     While performing recognition at OPERATION  306 , a determination may be made at DECISION OPERATION  308  whether the hybrid recognition rule  122  is triggered. As described above, during the rule-based recognition process, a sequence of rules may be matched with the audio  160  by the recognition system. If a portion of the audio  160  is not matched by a rule, the hybrid recognition rule  122  may be triggered. 
     If the hybrid recognition rule  122  is triggered, the method  300  may proceed to OPERATION  310 , where a hybrid recognition rule mark-up  220  may be inserted into the recognition result  210 . If the hybrid recognition rule  122  is not triggered, or after one or more hybrid recognition rule mark-ups  220  are inserted into the SVSRS recognition result  210 , the method  300  may then proceed to OPERATION  312 . 
     At OPERATION  312 , the SVSRS recognition result  210 , which may include SVSRS rule-matched recognition results  215  and/or hybrid recognition rule mark-ups  220  may be serialized and sent as a request  230  to the LVSRS  145 . The request  230  may also comprise the audio  160  and metadata  225 . As described above, the SVSRS recognition result  210  may be sent as a complete recognition result (including SVSRS rule-matched recognition results  215  and hybrid recognition rule mark-ups  220 ) after the SVSRS  115  has completed its local recognition, or may be sent as intermediate SVSRS recognition results while the user  105  is speaking. 
     Referring now to  FIG. 3B , if the audio  160  is sent to the LVSRS  145  for concurrent recognition (OPERATION  305 ), the method  300  may proceed to OPERATION  313 , where open-ended statistically-based recognition of the audio  160  may be performed by the LVSRS  145  while the SVSRS  115  is performed its rule-based recognition of the audio  160 . 
     The method  300  may proceed from OPERATION  312  ( FIG. 3A ) and optionally from OPERATION  313  to OPERATION  314 , where the request  230  (i.e., audio  160 , SVSRS recognition result  210 , and metadata  225 ) may be received and deserialized by the LVSRS  145 . 
     The method  300  may proceed from OPERATION  314  to DECISION OPERATION  316 , where the SVSRS recognition result  210  may be analyzed, and a determination may be made as to whether the SVSRS recognition result  210  comprises one or more hybrid recognition rule mark-ups  220 . 
     If a determination is made at DECISION OPERATION  316  that the SVSRS recognition result  210  comprises one or more hybrid recognition rule mark-ups  220 , the method  300  may proceed to OPERATION  317 , where if the audio  160  is already being recognized by the LVSRS  145  (OPERATION  313 ), the LVSRS  145  may cancel the recognition. 
     The method  300  may proceed to OPERATION  318 , where the LVSRS  145  may perform open-ended statistically-based recognition of the portions of the audio  160  specified by the one or more hybrid recognition rule mark-ups  220  in the SVSRS recognition result  210 . 
     At OPERATION  320 , the LVSRS  145  recognition result(s)  235 A-N may be combined with the SVSRS rule-matched recognition result(s)  215 A-N, and a hybrid recognition result  240  may be created. 
     The method  300  may optionally proceed from OPERATION  320  to OPERATION  322 , or from DECISION OPERATION  316  (if a determination is made that the SVSRS recognition results  210  does not comprise a hybrid recognition rule mark-up  220 ), where an arbitration algorithm may be applied. As described above, the arbitration algorithm is an algorithm pre-trained to reduce word error rates of recognitions. 
     At DECISION OPERATION  324 , a determination is made whether the SVSRS recognition result  210  or the LVSRS recognition result  235 , or, if the hybrid recognition rule  122  is triggered, the hybrid recognition result  240  has a better recognition quality based on predefined features. If the SVSRS recognition result  210  comprises a hybrid recognition rule mark-up  220 , the hybrid recognition result  240  may automatically be selected, serialized and sent to the SVSRS  115  (OPERATION  330 ). 
     If the SVSRS recognition result  210  does not comprise a hybrid recognition rule mark-up  220 , and if the SVSRS recognition result  210  is determined to have a higher recognition quality than the LVSRS recognition result  235  at DECISION OPERATION  324 , the method  300  may proceed to OPERATION  326 , where the SVSRS recognition result  210  may be serialized and sent to the SVSRS  115 . 
     If the SVSRS recognition result  210  does not comprise a hybrid recognition rule mark-up  220 , and if the LVSRS recognition result  235  is determined to have a higher recognition quality than the SVSRS recognition result  210  at DECISION OPERATION  324 , the method  300  may proceed to OPERATION  328 , where the LVSRS recognition result  235  may be serialized and sent to the SVSRS  115 . The method  300  may proceed to OPERATION  332  ( FIG. 3C ). 
     Referring now to  FIG. 3C , the method  300  may proceed from either OPERATION  326 ,  328 , or  330  to OPERATION  332 , where a response (which may be the SVSRS recognition result  210 , the LVSRS recognition result  235 , or the hybrid recognition result  240 ) may be received and deserialized. 
     At OPERATION  334 , a final result may be produced and applied to the task (e.g., to create a text message, perform an Internet search, transcribe notes into a notes application, etc.) for which the speech recognition feature was initiated. The method  300  may end at OPERATION  398 . 
     While the invention has been described in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a computer, those skilled in the art will recognize that the invention may also be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. 
     The embodiments and functionalities described herein may operate via a multitude of computing systems including, without limitation, desktop computer systems, wired and wireless computing systems, mobile computing systems (e.g., mobile telephones, netbooks, tablet or slate type computers, notebook computers, and laptop computers), hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, and mainframe computers. 
     In addition, the embodiments and functionalities described herein may operate over distributed systems (e.g., cloud-based computing systems), where application functionality, memory, data storage and retrieval and various processing functions may be operated remotely from each other over a distributed computing network, such as the Internet or an intranet. User interfaces and information of various types may be displayed via on-board computing device displays or via remote display units associated with one or more computing devices. For example user interfaces and information of various types may be displayed and interacted with on a wall surface onto which user interfaces and information of various types are projected. Interaction with the multitude of computing systems with which embodiments of the invention may be practiced include, keystroke entry, touch screen entry, voice or other audio entry, gesture entry where an associated computing device is equipped with detection (e.g., camera) functionality for capturing and interpreting user gestures for controlling the functionality of the computing device, and the like. 
       FIGS. 4-6  and the associated descriptions provide a discussion of a variety of operating environments in which embodiments of the invention may be practiced. However, the devices and systems illustrated and discussed with respect to  FIGS. 4-6  are for purposes of example and illustration and are not limiting of a vast number of computing device configurations that may be utilized for practicing embodiments of the invention, described herein. 
       FIG. 4  is a block diagram illustrating physical components (i.e., hardware) of a computing device  400  with which embodiments of the invention may be practiced. The computing device components described below may be suitable for the device  110  and/or server  130  described above. In a basic configuration, the computing device  400  may include at least one processing unit  402  and a system memory  404 . Depending on the configuration and type of computing device, the system memory  404  may comprise, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. The system memory  404  may include an operating system  405  and one or more program modules  406  suitable for running software applications  420 , such as the SVSRS  115 . The operating system  405 , for example, may be suitable for controlling the operation of the computing device  400 . Furthermore, embodiments of the invention may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in  FIG. 4  by those components within a dashed line  408 . The computing device  400  may have additional features or functionality. For example, the computing device  400  may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in  FIG. 4  by a removable storage device  409  and a non-removable storage device  410 . 
     As stated above, a number of program modules and data files may be stored in the system memory  404 . While executing on the processing unit  402 , the program modules  406  may perform processes including, but not limited to, one or more of the stages of the method  300  illustrated in  FIGS. 3A-C . Other program modules that may be used in accordance with embodiments of the present invention may include applications such as electronic mail and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, etc. 
     Furthermore, embodiments of the invention may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. For example, embodiments of the invention may be practiced via a system-on-a-chip (SOC) where each or many of the components illustrated in  FIG. 4  may be integrated onto a single integrated circuit. Such an SOC device may include one or more processing units, graphics units, communications units, system virtualization units and various application functionality all of which are integrated (or “burned”) onto the chip substrate as a single integrated circuit. When operating via an SOC, the functionality, described herein, with respect to providing incorporation of an exogenous large-vocabulary model  150  into rule-based speech recognition may be operated via application-specific logic integrated with other components of the computing device  400  on the single integrated circuit (chip). Embodiments of the invention may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the invention may be practiced within a general purpose computer or in any other circuits or systems. 
     The computing device  400  may also have one or more input device(s)  412  such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc. The output device(s)  414  such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used. The computing device  400  may include one or more communication connections  416  allowing communications with other computing devices  418 . Examples of suitable communication connections  416  include, but are not limited to, RF transmitter, receiver, and/or transceiver circuitry; universal serial bus (USB), parallel, and/or serial ports. 
     The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, or program modules. The system memory  404 , the removable storage device  409 , and the non-removable storage device  410  are all computer storage media examples (i.e., memory storage.) Computer storage media may include RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other article of manufacture which can be used to store information and which can be accessed by the computing device  400 . Any such computer storage media may be part of the computing device  400 . Computer storage media does not include a carrier wave or other propagated or modulated data signal. 
     Communication media may be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. 
       FIGS. 5A and 5B  illustrate a mobile computing device  500 , for example, a mobile telephone, a smart phone, a tablet personal computer, a laptop computer, and the like, with which embodiments of the invention may be practiced. With reference to FIG.  5 A, one embodiment of a mobile computing device  500  for implementing the embodiments is illustrated. In a basic configuration, the mobile computing device  500  is a handheld computer having both input elements and output elements. The mobile computing device  500  typically includes a display  505  and one or more input buttons  510  that allow the user to enter information into the mobile computing device  500 . The display  505  of the mobile computing device  500  may also function as an input device (e.g., a touch screen display). If included, an optional side input element  515  allows further user input. The side input element  515  may be a rotary switch, a button, or any other type of manual input element. In alternative embodiments, mobile computing device  500  may incorporate more or less input elements. For example, the display  505  may not be a touch screen in some embodiments. In yet another alternative embodiment, the mobile computing device  500  is a portable phone system, such as a cellular phone. The mobile computing device  500  may also include an optional keypad  535 . Optional keypad  535  may be a physical keypad or a “soft” keypad generated on the touch screen display. In various embodiments, the output elements include the display  505  for showing a graphical user interface (GUI), a visual indicator  520  (e.g., a light emitting diode), and/or an audio transducer  525  (e.g., a speaker). In some embodiments, the mobile computing device  500  incorporates a vibration transducer for providing the user with tactile feedback. In yet another embodiment, the mobile computing device  500  incorporates input and/or output ports, such as an audio input (e.g., a microphone jack), an audio output (e.g., a headphone jack), and a video output (e.g., a HDMI port) for sending signals to or receiving signals from an external device. 
       FIG. 5B  is a block diagram illustrating the architecture of one embodiment of a mobile computing device. That is, the mobile computing device  500  can incorporate a system (i.e., an architecture)  502  to implement some embodiments. In one embodiment, the system  502  is implemented as a “smart phone” capable of running one or more applications (e.g., browser, e-mail, calendaring, contact managers, messaging clients, games, and media clients/players). In some embodiments, the system  502  is integrated as a computing device, such as an integrated personal digital assistant (PDA) and wireless phone. 
     One or more application programs  520  may be loaded into the memory  562  and run on or in association with the operating system  564 . Examples of the application programs include phone dialer programs, e-mail programs, personal information management (PIM) programs, word processing programs, spreadsheet programs, Internet browser programs, messaging programs, and so forth. The SVSRS  115  may also be loaded into the memory  562  and run on or in associated with the operation system  564 . The system  502  also includes a non-volatile storage area  568  within the memory  562 . The non-volatile storage area  568  may be used to store persistent information that should not be lost if the system  502  is powered down. The application programs  520  may use and store information in the non-volatile storage area  568 , such as e-mail or other messages used by an e-mail application, and the like. A synchronization application (not shown) also resides on the system  502  and is programmed to interact with a corresponding synchronization application resident on a host computer to keep the information stored in the non-volatile storage area  568  synchronized with corresponding information stored at the host computer. As should be appreciated, other applications may be loaded into the memory  562  and run on the mobile computing device  500 . 
     The system  502  has a power supply  570 , which may be implemented as one or more batteries. The power supply  570  might further include an external power source, such as an AC adapter or a powered docking cradle that supplements or recharges the batteries. 
     The system  502  may also include a radio  572  that performs the function of transmitting and receiving radio frequency communications. The radio  572  facilitates wireless connectivity between the system  502  and the “outside world,” via a communications carrier or service provider. Transmissions to and from the radio  572  are conducted under control of the operating system  564 . In other words, communications received by the radio  572  may be disseminated to the application programs  520  via the operating system  564 , and vice versa. 
     The visual indicator  520  may be used to provide visual notifications and/or an audio interface  574  may be used for producing audible notifications via the audio transducer  525 . In the illustrated embodiment, the visual indicator  520  is a light emitting diode (LED) and the audio transducer  525  is a speaker. These devices may be directly coupled to the power supply  570  so that when activated, they remain on for a duration dictated by the notification mechanism even though the processor  560  and other components might shut down for conserving battery power. The LED may be programmed to remain on indefinitely until the user takes action to indicate the powered-on status of the device. The audio interface  574  is used to provide audible signals to and receive audible signals from the user. For example, in addition to being coupled to the audio transducer  525 , the audio interface  574  may also be coupled to a microphone to receive audible input, such as to facilitate a telephone conversation. In accordance with embodiments of the present invention, the microphone may also serve as an audio sensor to facilitate control of notifications, as will be described below. The system  502  may further include a video interface  576  that enables an operation of an on-board camera  530  to record still images, video stream, and the like. 
     A mobile computing device  500  implementing the system  502  may have additional features or functionality. For example, the mobile computing device  500  may also include additional data storage devices (removable and/or non-removable) such as, magnetic disks, optical disks, or tape. Such additional storage is illustrated in  FIG. 5B  by the non-volatile storage area  568 . 
     Data/information generated or captured by the mobile computing device  500  and stored via the system  502  may be stored locally on the mobile computing device  500 , as described above, or the data may be stored on any number of storage media that may be accessed by the device via the radio  572  or via a wired connection between the mobile computing device  500  and a separate computing device associated with the mobile computing device  500 , for example, a server computer in a distributed computing network, such as the Internet. As should be appreciated such data/information may be accessed via the mobile computing device  500  via the radio  572  or via a distributed computing network. Similarly, such data/information may be readily transferred between computing devices for storage and use according to well-known data/information transfer and storage means, including electronic mail and collaborative data/information sharing systems. 
       FIG. 6  illustrates one embodiment of the architecture of a system for providing incorporation of an exogenous large-vocabulary model  150  into rule-based speech recognition, as described above. Content developed, interacted with, or edited in association with LVSRS  145  may be stored in different communication channels or other storage types. For example, various documents may be stored using a directory service  622 , a web portal  624 , a mailbox service  626 , an instant messaging store  628 , or a social networking site  630 . The LVSRS  145  may use any of these types of systems or the like for providing incorporation of an exogenous large-vocabulary model  150  into rule-based speech recognition, as described herein. A server  130  may provide LVSRS  145  to clients. As one example, the server  130  may be a web server providing LVSRS  145  over the web. The server  130  may provide LVSRS  145  over the web to clients through a network  140 . By way of example, the client computing device may be implemented and embodied in a personal computer  605 A, a tablet computing device  605 B and/or a mobile computing device  605 C (e.g., a smart phone), or other computing device. Any of these embodiments of the client computing device may obtain content from the store  616 . 
     Embodiments of the present invention, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the invention. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     The description and illustration of one or more embodiments provided in this application are not intended to limit or restrict the scope of the invention as claimed in any way. The embodiments, examples, and details provided in this application are considered sufficient to convey possession and enable others to make and use the best mode of claimed invention. The claimed invention should not be construed as being limited to any embodiment, example, or detail provided in this application. Regardless of whether shown and described in combination or separately, the various features (both structural and methodological) are intended to be selectively included or omitted to produce an embodiment with a particular set of features. Having been provided with the description and illustration of the present application, one skilled in the art may envision variations, modifications, and alternate embodiments falling within the spirit of the broader aspects of the general inventive concept embodied in this application that do not depart from the broader scope of the claimed invention.