Abstract:
Methods and systems are provided for shaping speech dialog of a speech system. In one embodiment, a method includes: receiving data related to a first utterance from a user of the speech system; processing the data based on at least one attribute processing technique that determines at least one attribute of the first utterance; determining a shaping pattern based on the at least one attribute; and generating a speech prompt based on the shaping pattern.

Description:
TECHNICAL FIELD 
       [0001]    The technical field generally relates to speech systems, and more particularly relates to methods and systems for shaping dialog within a speech system. 
       BACKGROUND 
       [0002]    Vehicle speech recognition systems perform speech recognition or understanding of speech uttered by occupants of the vehicle. The speech utterances typically include commands that communicate with or control one or more features of the vehicle or other systems that are accessible by the vehicle. Speech recognition performance may vary depending on attributes of the user&#39;s speech such as, rhythm, vocabulary, verbosity, dialect, accent, etc. 
         [0003]    A speech dialog system generates speech prompts in response to the speech utterances. In some instances, the speech prompts are generated in response to the speech recognition system needing further information in order to perform the speech recognition. For example, a speech prompt may ask the user to repeat the speech utterance or may ask the user to select from a list of possibilities. In some instances, such speech prompts may result in the receipt of a speech utterance that fails to resolve the recognition issue. 
         [0004]    Accordingly, it is desirable to provide improved methods and systems for shaping a speech dialog to improve the speech recognition. Accordingly, it is further desirable to provide methods and systems for shaping the speech dialog based on attributes of the user&#39;s speech. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. 
       SUMMARY 
       [0005]    Methods and systems are provided for shaping speech dialog of a speech system. In one embodiment, a method includes: receiving data related to a first utterance from a user of the speech system; processing the data based on at least one attribute processing technique that determines at least one attribute of the first utterance; determining a shaping pattern based on the at least one attribute; and generating a speech prompt based on the shaping pattern. 
         [0006]    In another embodiment, a speech system includes a first module that receives data related to a first utterance from a user of the speech system. A second module processes the data based on at least one attribute processing technique that determines at least one attribute of the first utterance. A third module determines a shaping pattern based on the at least one attribute. A fourth module generates a speech prompt based on the shaping pattern. 
     
    
     
       DESCRIPTION OF THE DRAWINGS 
         [0007]    The exemplary embodiments will hereinafter be described in conjunction with the following drawing figures, wherein like numerals denote like elements, and wherein: 
           [0008]      FIG. 1  is a functional block diagram of a vehicle that includes a speech system in accordance with various exemplary embodiments; 
           [0009]      FIG. 2  is a dataflow diagram illustrating a speech system in accordance with various exemplary embodiments; and 
           [0010]      FIG. 3  is a flowchart illustrating a speech method that may be performed by the speech system in accordance with various exemplary embodiments. 
       
    
    
     DETAILED DESCRIPTION 
       [0011]    The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. As used herein, the term module refers to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. 
         [0012]    In accordance with exemplary embodiments of the present disclosure a speech system  10  is shown to be included within a vehicle  12 . In various exemplary embodiments, the speech system  10  provides speech recognition and a dialog for one or more vehicle systems through a human machine interface module (HMI) module  14 . Such vehicle systems may include, for example, but are not limited to, a phone system  16 , a navigation system  18 , a media system  20 , a telematics system  22 , a network system  24 , or any other vehicle system that may include a speech dependent application. As can be appreciated, one or more embodiments of the speech system  10  can be applicable to other non-vehicle systems having speech dependent applications and thus, is not limited to the present vehicle example. 
         [0013]    The speech system  10  communicates with the HMI module and/or the multiple vehicle systems  14 - 24  through a communication bus and/or other communication means  26  (e.g., wired, short range wireless, or long range wireless). The communication bus can be, for example, but is not limited to, a controller area network (CAN) bus, local interconnect network (LIN) bus, or any other type of bus. 
         [0014]    The speech system  10  includes a speech recognition module  32 , a dialog manager module  34 , and a speech generation module  35 . As can be appreciated, the speech recognition module  32 , the dialog manager module  34 , and the speech generation module  35  may be implemented as separate systems and/or as a combined system as shown. In general, the speech recognition module  32  receives and processes speech utterances from the HMI module  14  using one or more speech recognition techniques (e.g., front end feature extraction may be used that is followed by a Hidden Markov Model (HMM) and scoring mechanism). The speech recognition module  32  generates results of possible recognized speech and an associated confidence score based on the processing. 
         [0015]    The dialog manager module  34  manages an interaction sequence and a selection of speech prompts to be spoken to the user based on the results of the recognition. In particular, the dialog manager module  34  includes a dialog shaping module  36  ( FIG. 2 ) that detects one or more attributes of the speech utterance and adapts a speech prompt based on the detection. In various embodiments, the attributes include, but are not limited to, a rhythm, a vocabulary, a verbosity, a dialect, and an accent. The speech generation module  35  generates the spoken prompts to the user based on the adapted speech prompt determined by the dialog manager  34 . In other words, the speech generation module  35  converts the text of the speech prompt to a spoken prompt that is issued to the user by the HMI module  14 . 
         [0016]    Referring now to  FIG. 2 , a dataflow diagram illustrates the dialog shaping module  36  in accordance with various exemplary embodiments. As can be appreciated, various exemplary embodiments of the dialog shaping module  36 , according to the present disclosure, may include any number of sub-modules. In various exemplary embodiments, the sub-modules shown in  FIG. 2  may be combined and/or further partitioned to similarly shape the dialog based on attributes of a speech utterance. In various exemplary embodiments, the dialog shaping module  36  includes an attribute detection module  40 , a learning and adaptation module  42 , a pattern module  44 , and a dialog manager module  46 . 
         [0017]    The attribute detection module  40  receives as input data including a speech utterance  48  and results  50  or any other partially processed representation of the utterance from the recognizer module  32  ( FIG. 1 ) (hereinafter generally referred to as a speech utterance  48  and results  50 . As discussed above, the recognizer module  32  ( FIG. 1 ) processes a speech utterance (e.g., received from the HMI module  14  ( FIG. 1 ) using one or more speech models to determine the results  50 . If the results  50  indicate a low confidence scored (e.g., below a threshold), the attribute detection module  40  processes the speech utterance  48  and/or the results  50  to identify one or more attributes  52  of the speech utterance  48  and/or attribute qualities  54  of the speech utterance  48 . 
         [0018]    In various embodiments, the attribute detection module  40  identifies the attributes  52  and/or the attribute qualities  54  based on one or more attribute processing techniques. For example, the attribute processing techniques may be based on Hidden Markov Models, or other models known in the art for identifying a particular attribute. In various embodiments, the attribute processing techniques are based on human attributes such as, but not limited to, human speech behaviors, and demographics. Such human attributes may include, but are not limited to, a rhythm of the speech, a vocabulary used in the speech, a verbosity of the speech, a dialect of the speech, and/or an accent of the speech. 
         [0019]    In various embodiments, the attribute processing techniques are further based on attribute qualities  54  that are associated with the human attributes. For example, attribute qualities  54  associated with the rhythm of the speech may include, but are not limited, slow, fast, normal, or a specific pace. In another example, attribute qualities  54  associated with the vocabulary of the speech may include, but are not limited, specific vocabulary that is commonly used or recognized and specific vocabulary that is not commonly used or recognized. In other examples, attribute qualities  54  associated with the verbosity of the speech may include, but are not limited, verbose, and non-verbose. In still other examples, attribute qualities  54  associated with the dialect type may include, but are not limited to, specific dialects that are commonly used or easily recognized, and specific dialects that are not commonly used or recognized. Attribute qualities  54  associated with the accent type may include, but are not limited to, specific accents that are commonly used or easily recognized, and specific accents that are not commonly used or recognized. 
         [0020]    The learning and adaptation module  42  receives as input the attributes  52  and/or the attribute qualities  54  that were identified by the attribute detection module  40 . The learning and adaptation module  42  evaluates the attributes  52  and/or the attribute qualities  54  and selects a cause  56  of the low confidence score associated with the results  50 . The cause  56  may be, for example, the verbosity quality indicates verbose, the rhythm quality indicates too fast, etc. 
         [0021]    In various embodiments, the learning and adaptation module  42  selects the cause based on a set of rules that associate an attribute  52  and/or attribute quality  54  to a particular cause. In various other embodiments, the learning and adaptation module  42  learns the cause  56  by learning a relationship between the attribute  52  and/or the attribute quality  54  and the cause  56  through iterations of the recognition process. In various embodiments, the learning techniques may select a most probable cause or may explore recognition results in order to find other causes. 
         [0022]    As can be appreciated, the learning and adaptation module  42  may identify one or more causes  56 . If multiple causes  56  are identified, the multiple causes may be arbitrated based on a priority scheme to identify a most influential cause. Alternatively, the multiple causes may not be arbitrated and the multiple causes are provided for consideration by the pattern module  44 . 
         [0023]    The pattern module  44  receives as input the identified cause or causes  56 . The pattern module  44  determines a shaping pattern  58  based on the identified cause or causes  56 . The shaping pattern  58  includes a pattern for modifying or shaping a predefined prompt based on the cause or causes  56 . The shaping pattern modifies an attribute and/or an attribute quality of a speech prompt. In various embodiments, a particular shaping pattern  58  may be directly associated with a particular cause. For example, if the identified cause indicates that the rhythm of the speech utterance was too fast, a pattern that lowers the rhythm or pace of the predefined prompt may be selected. In another example, if the identified cause indicates that the speech utterance was too verbose, a pattern that lowers the verbosity of the predefined prompt may be selected. In yet another example, if the identified cause indicates that the speech utterance was due to an uncommonly used dialect or accent, a pattern that modifies an accent of the prompt to be similar to the speaker&#39;s accent but more recognizable to the system may be selected. 
         [0024]    As can be appreciated, the pattern module  44  may identify one or more shaping patterns  58  based on the one or more causes  56 . If multiple shaping patterns are identified, the multiple patterns may be arbitrated based on a priority scheme to identify a best pattern. Alternatively, the multiple patterns may be combined to define a single pattern. 
         [0025]    The dialog manager module  46  receives as input the shaping pattern  58  and a predefined speech prompt  60 . In various embodiments, the predefined speech prompt  60  may be a prompt that requests further information from the user. The dialog manager module  46  generates a speech prompt  62  based on the shaping pattern  58  and the predefined speech prompt  60 . For example, the dialog manager module  46  shapes or modifies the predefined speech prompt  60  by applying the shaping pattern  58  to the predefined speech prompt  60 . In various embodiments, the generated speech prompt  62  is in a text format and may be converted to a spoken format and generated to the user, for example, via the HMI module  14  ( FIG. 1 ). 
         [0026]    Referring now to  FIG. 3  and with continued reference to  FIG. 2 , a flowchart illustrates a speech method that may be performed by the speech system  10  in accordance with various exemplary embodiments. As can be appreciated in light of the disclosure, the order of operation within the method is not limited to the sequential execution as illustrated in  FIG. 3 , but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. As can further be appreciated, one or more steps of the method may be added or removed without altering the spirit of the method. 
         [0027]    As shown, the method may begin at  99 . The speech utterance  48  is received at  100 . One or more speech recognition methods are performed on the speech utterance  48  to determine the results  50  at  110 . The results  50  are evaluated at  120 . If a confidence score associated with the results  50  is high (e.g., above a threshold), then the method may end at  130 . 
         [0028]    If, however, the confidence score associated with the results  50  is low (e.g., below a threshold) at  120 , then the speech utterance  48  and/or the results  50  is further processed based on one or more attribute processing techniques to identify one or more attributes  52  and/or attribute qualities  54  at  140 . One or more causes  56  of the low confidence score is determined at  150  based on the one or more attributes  52  and/or one or more attribute qualities  54 . A shaping pattern  58  is determined based on the one or more causes  56  at  160 . The shaping pattern  58  is then used to shape or modify a speech prompt  60  at  170 . Thereafter, the shaped or modified speech prompt  62  is generated as a spoken command to the user at  180  and the method may end at  130 . 
         [0029]    While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof