Patent Publication Number: US-7908142-B2

Title: Apparatus and method for identifying prosody and apparatus and method for recognizing speech

Description:
CROSS REFERENCES TO RELATED APPLICATIONS 
     The present invention contains subject matter related to Japanese Patent Application JP 2006-145729 filed in the Japanese Patent Office on May 25, 2006, the entire contents of which are incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to an apparatus and a method for identifying prosody and an apparatus and a method for recognizing speech. More particularly, the present invention relates to an apparatus and a method for identifying prosody on the basis of features of input speech and an apparatus and a method for recognizing speech using the prosody identification. 
     2. Description of the Related Art 
     In recent years, use of speech recognition technologies has become widespread. In known speech recognition technologies, information about phonemes (hereinafter referred to as “phoneme information”) contained in speech among other information is recognized. In widely used speech recognition technologies, information about prosody (hereinafter referred to as “prosody information”), which is one kind of information contained in speech other than phonemes, is not actively utilized. 
     However, some known technologies utilize prosody information. For example, a technology described in Japanese Unexamined Patent Application Publication No. 04-66999 utilizes prosody information in order to more appropriately determine the position of a border between phonetic syllables. However, in this technology, the prosody information is utilized as supplementary information to improve the accuracy of the speech recognition. That is, a variety of information items contained in the prosody information is not explicitly identified. 
     SUMMARY OF THE INVENTION 
     In some cases, human utterance speech cannot be identified by using phonetic information. For example, in Japanese language, “UN” that indicates an affirmative answer is phonetically similar to “UUN” that indicates a negative answer. In such a case, the affirmative answer cannot be distinguished from the negative answer using only the phonetic information contained in an utterance. Accordingly, the utterance needs be identified on the basis of prosody information, for example, an intonation pattern and a phoneme duration. 
     In speech signal processing, a process related to intonation recognition is generally performed by detecting a pitch frequency (or a pitch cycle). However, when a pitch frequency is detected, an error tends to occur due to an effect of noise. In addition, when a pitch frequency is detected from a quiet voice or a voice having small pitch features, an error tends to occur. In such a situation in which a detection error of a pitch frequency tends to occur or for an utterance that easily causes a detection error, it is difficult to apply an identification method using prosody information. 
     Accordingly, the present invention provides an apparatus and a method for identifying prosody and an apparatus and a method for recognizing speech capable of effectively detecting a pitch frequency even when a voice that is adversely effected by noise, a quiet voice, or a voice having small pitch features is input and reliably recognizing the input voice on the basis of prosodic features. 
     According to an embodiment of the present invention, a prosody identifying apparatus identifies input speech using an amount of change in movement of a feature distribution obtained from the frequency characteristic of the input speech without detecting a pitch frequency. In addition, a desired components of an autocorrelation matrix of the frequency characteristic of the input speech in a diagonal direction are used as the feature distribution. 
     That is, according to the embodiment of the present invention, to address the above-described problem, when identifying input speech on the basis of prosodic features of the input speech, the prosody identifying apparatus identifies the input speech using an amount of change in movement of a feature distribution obtained from an autocorrelation matrix of the frequency characteristic of the input speech. 
     Here, a time difference between median points of the feature distribution can be utilized as the amount of change in movement of the feature distribution. In addition, a desired components of an autocorrelation matrix of the frequency characteristic of the input speech in a diagonal direction can be used as the feature distribution. 
     According to another embodiment of the present invention, a speech recognition apparatus includes inputting means for inputting a speech signal, prosody identifying means for identifying prosody on the basis of an amount of change in movement of a feature distribution obtained from an autocorrelation matrix of a frequency characteristic of the input speech input to the inputting means, and speech recognizing means for performing speech recognition on the basis of features acquired by sound-analyzing the speech input to the inputting means, and selecting means for selecting whether to output an output of the speech recognizing means or to integrate the output from the prosody identifying means with an output of the speech recognizing means and output the integrated output. Subsequently, the selecting means outputs the selected output. 
     According to the above-described embodiments of the present invention, when identifying input speech on the basis of prosodic features of the input speech, the prosody identifying apparatus and the speech recognition apparatus identify the input speech using an amount of change in movement of a feature distribution obtained from the frequency characteristic of the input speech. Accordingly, even when a voice that is adversely effected by noise, a quiet voice, or a voice having small pitch features is input, the input voice can be identified using a more robust approach than in the known methods. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an exemplary system configuration of a prosody identifying apparatus according to an embodiment of the present invention; 
         FIG. 2A  illustrates an example of a frequency characteristic of an ordinary voice; 
         FIG. 2B  illustrates an example of a frequency characteristic of a quiet voice; 
         FIG. 3  is a flow chart illustrating frequency characteristic analysis. 
         FIGS. 4A-4C  are graphs illustrating processes performed by a frequency characteristic analyzing section according to an embodiment of the present invention; 
         FIG. 5  is a flow chart illustrating a process performed by a feature distribution moving variation computing section according to the embodiment of the present invention; 
         FIG. 6  illustrates an autocorrelation matrix of a frequency characteristic; 
         FIG. 7  illustrates components of the autocorrelation matrix of a frequency characteristic in a diagonal direction; 
         FIG. 8  is a diagram illustrating a graph of the autocorrelation matrix of a frequency characteristic and components of the autocorrelation matrix in a diagonal direction; 
         FIGS. 9A-9C  illustrate feature distributions of vectors corresponding to three orders of components of the autocorrelation matrix of a frequency characteristic in the diagonal direction; 
         FIGS. 10A-10I  illustrate a change over time in an order of the median point of a feature distribution and a change over time in a time difference between the orders of the median point in the case of an ordinary voice according to the embodiment; 
         FIGS. 11A-11I  illustrate a change over time in an order of the median point of a feature distribution and a change over time in a time difference between the orders of the median point in the case of a quiet voice according to the embodiment; 
         FIG. 12  is a block diagram illustrating an exemplary system in which a prosody identifying apparatus according to the embodiment is used together with a widely used speech recognition apparatus; and 
         FIG. 13  is a flow chart illustrating the operation performed by the system shown in  FIG. 12 . 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Exemplary embodiments of the present invention are described in detail below with reference to the accompanying drawings. 
     An exemplary system configuration and the entire process flow of a prosody identifying apparatus are described first. Thereafter, the internal process performed by a sound analyzing unit of the prosody identifying apparatus is described in detail. Subsequently, an exemplary system configuration is described in detail when the prosody identifying apparatus according to an embodiment of the present invention is used together with a widely used speech recognition apparatus. 
     Speech Recognition 
       FIG. 1  is a block diagram illustrating an exemplary system configuration of a prosody identifying apparatus according to an embodiment of the present invention. Basically, the system configuration of the prosody identifying apparatus is similar to that of a widely used speech recognition apparatus. As shown in  FIG. 1 , the prosody identifying apparatus includes an input unit  11 , a sound analyzing unit  12 , an identification unit  13 , and an output unit  15 . 
     The input unit  11  includes a device for inputting a sound signal (e.g., a microphone), an amplifier for amplifying the input signal, and an analog-to-digital (A/D) converter for converting the amplified signal to a digital signal. The input unit  11  samples the input signal, for example, at  16  kHz sampling rate, and delivers the sampled signal to the sound analyzing unit  12 . 
     The sound analyzing unit  12  retrieves features required for recognition from the input speech signal. Thereafter, the sound analyzing unit  12  delivers the retrieved features to the identification unit  13 . The internal process performed by the sound analyzing unit  12  according to the present embodiment will be described later. 
     The identification unit  13  performs a recognition process of the unknown speech data using parameters stored in a parameter storage unit  14 . The parameters stored in a parameter storage unit  14  are generated on the basis of features obtained by sound-analyzing learning speech data in advance. 
     As used herein, the term “recognition process of the unknown speech data” refers to a process for selecting prosody identification units in accordance with the input from a given prosody-identification-unit dictionary. Examples of the typical recognition methods include a dynamic programming (DP) matching method, a neural network method, and a hidden Markov model (HMM) method. 
     In the method using the DP matching, standard patterns known as templates are found as a parameter from the features obtained by analyzing speech signals in advance. By comparing the templates with the features of the unknown speech, the template that is the most closest to the features is found. To compensate for variation in the utterance speed, a technique called “dynamic time warping” is widely used. In this technique, the time axis is shrunk or stretched so that the distortion between the features and the template is minimized. 
     In the method using a neural network, recognition is made using a network model that simulates the structure of a human brain. In a learning step, a weighting coefficient for each of paths is determined as a parameter in advance. A distance between the output obtained by inputting the features of the unknown speech to the network and each of the prosody identification units in the prosody-identification-unit dictionary is computed so that the prosody identification unit corresponding to the input speech signal is determined. 
     In the method using the HMM, recognition is made using a probability model. Transition probability and output symbol probability are determined for a state transition model on the basis of learning data in advance. Using the occurrence probability of each of the models for the features of the unknown speech, a prosody identification unit corresponding to the input speech is determined. 
     As noted above, in general, the recognition process performed in the identification unit  13  includes a learning step and a recognition step. In the learning step, a parameter determined by learning data, that is, a template, a weighting coefficient of a network model, or a statistic parameter of a probability model, is obtained and stored in the parameter storage unit  14 . Subsequently, in the recognition step, the input unknown speech signal is sound-analyzed. Thereafter, a score based on a distance or an occurrence probability, depending on the recognition method, is assigned to each of the prosody identification units in a given prosody-identification-unit dictionary. Thus, the prosody identification unit having the highest score or a plurality of the prosody identification units having the highest scores from the top are selected as a recognition result. 
     The identification unit  13  delivers such a recognition result to the output unit  15 . 
     The output unit  15  displays the delivered recognition result on a screen, outputs the recognition result in the form of sound, or instructs a different unit to operate in accordance with the recognition result. 
     In the known methods, to detect a pitch frequency, it is assumed that a time duration of a pitch cycle, which is a cycle of the vibration of the vocal cords in an utterance, (or a pitch frequency, which is the inverse of the pitch cycle) is uniquely determined. The process of uniquely determining the pitch frequency is a process of finding the center frequency of a peak component that is present in the lowest frequency range in the frequency characteristic distribution corresponding to an utterance. For example, in the case of a frequency characteristic of an ordinary voice shown in  FIG. 2A , a frequency fp represents the pitch frequency. 
     However, if the utterance contains noise or the utterance is made by a quiet voice having small pitch features and the known methods are used, it is difficult to perform the process of determining the pitch frequency. For example, in the case of a quiet voice shown in  FIG. 2B , it is difficult to detect the center frequency of a peak component that is present in the lowest frequency range. This is because the determination of the pitch frequency depends on the peaks of the frequency characteristic. 
     In contrast, in the prosody identifying apparatus according to the present embodiment, the prosody identifying apparatus uses an amount of change in movement of a feature distribution. Thus, the prosody identifying apparatus does not require the process of uniquely determining the appropriate pitch frequency that depends on the peaks of the frequency characteristic. Consequently, even when it is difficult to detect the pitch frequency, the prosody identifying apparatus can detect a change in intonation using a more robust approach than in the known methods. 
     Additionally, in the known methods of detecting a pitch frequency, the frequency characteristic corresponding to an utterance is considered to be a feature distribution. Then, desired features (pitch frequency) are extracted from the distribution. According to the present embodiment, by using desired components of an autocorrelation matrix in the diagonal direction, a change in movement of the frequency characteristic can be obtained from a plurality of feature distributions. Thus, the change in intonation in the utterance can be appropriately obtained. 
     Such a key feature of the embodiment of the present invention is mainly provided by the sound analyzing unit  12  shown in  FIG. 1 . The exemplary configuration and operations of the sound analyzing unit  12  are described in detail below. 
     Internal Process of Sound Analyzing Unit 
     According to the present embodiment, as shown in  FIG. 1 , the sound analyzing unit  12  includes a frequency characteristic analyzing section  21  and a feature distribution moving variation computing section  22 . 
     The frequency characteristic analyzing section  21  converts an input speech signal to a frequency characteristic. The process performed by the frequency characteristic analyzing section  21  is described in detail next with reference to a flow chart shown in  FIG. 3 . 
     As shown in  FIG. 3 , at step S 31 , the input speech signal is converted to a frequency domain by means of a time-frequency transform process, such as Fast Fourier Transform (FFT) analysis. Thus, a general characteristic is acquired. An example of the frequency characteristic is shown in  FIG. 4A . 
     Subsequently, the processing proceeds to step S 32 , where the frequency axis of the general frequency characteristic is logarithmized so that a frequency characteristic on a logarithmic frequency scale is obtained. An example of the frequency characteristic on a logarithmic frequency scale is shown in  FIG. 4B . 
     Subsequently, the processing proceeds to step S 33 , where only a desired frequency range is extracted from the frequency characteristic on a logarithmic frequency scale. This result is output as the result of the frequency characteristic analyzing section  21 .  FIG. 4C  illustrates the frequency characteristic of only a desired frequency range Rw retrieved from the frequency characteristic shown in  FIG. 4B . 
     The frequency characteristic, as shown in  FIG. 4C , which is the above-described result of analysis performed by the frequency characteristic analyzing section  21 , is delivered to the feature distribution moving variation computing section  22 . 
     The process flow of the feature distribution moving variation computing section  22  is described in detail next with reference to a flow chart shown in  FIG. 5 . 
     At step S 41 , the feature distribution moving variation computing section  22  computes an autocorrelation matrix of a frequency characteristic using the delivered frequency characteristic. 
     Here, the frequency characteristic delivered from the frequency characteristic analyzing section  21  is expressed in the form of a column vector V of size N. The column vector V is given by the following expression:
 
 V =( v   1   , v   2   , . . . , v   i   , . . . , v   j   , . . . , v   n ) T    (1)
 
     At that time, an autocorrelation matrix M is expressed as a product of the vector V and a transposed matrix V′ as follows:
 
 M=V·V   T    (2)
 
       FIG. 6  illustrates the autocorrelation matrix M given by equation (2). 
     In a lower triangular matrix L (a lower left triangular section of  FIG. 7 ) of the autocorrelation matrix M, the ith-order components of the lower triangular matrix L in the diagonal direction form a vector formed from the products of (i−1+j)th-order coefficient and the jth-order coefficient of the vector V, where 1≦i≦N and 1≦j≦(N−(i−1)). 
     Let a vector D i  denote the ith-order components of the lower triangular matrix L in the diagonal direction. Then, D i  is expressed as follows: 
     
       
         
           
             
               
                 
                   
                     
                       
                         
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     Since the vector V represents the frequency characteristic on a logarithmic frequency scale, the vector D i  indicates a relationship between any frequency (in the frequency range of the frequency characteristic) and a frequency being an integer multiple of the frequency (i.e., a frequency distant from the frequency by (i−1) orders on the logarithmic frequency scale). A vector D 1  formed from the first-order components of the lower triangular matrix L (i.e., the main diagonal components) represents a vector formed from the squares of the amplitudes of frequency components of the frequency characteristic (i.e., the power spectrum). 
     After computing the autocorrelation matrix M of the frequency characteristic, the processing proceeds to step S 42  shown in  FIG. 5 . At step S 42 , a desired diagonal-component vector D i  is retrieved from the lower triangular matrix L of the autocorrelation matrix M, where i denotes any number between 1 and N. These diagonal-component vectors D i  are defined as feature distributions for identification. 
     When the entire feature distributions are defined as a set D of the vectors D i , the set D is expressed as follows:
 
D={D i }  (4)
 
where i denotes any number between 1 and N.
 
     The entire feature distributions, that is, the set of desired component vectors D i  in the diagonal direction is schematically shown in  FIG. 7 . In  FIG. 7 , diagonal solid lines (solid lines slanting down to the right) a to d represent the desired component vectors D i . 
     An example of such a feature distribution is illustrated in  FIG. 8  and  FIGS. 9A to 9C . In  FIG. 8 , the vectors a to c are selected. The orders of the vectors a to c correspond to a frequency to the power of two, a frequency to the power of three, and a frequency to the power of four, respectively. That is, on the logarithmic frequency scale, the logarithmic frequencies corresponding to the ith orders are log (2), log (3), and log (4). The feature distributions of these component vectors a to c are illustrated in  FIGS. 9A to 9C , respectively. That is,  FIGS. 9A to 9C  illustrate the feature distributions of component vectors having logarithmic frequencies of 2, 3, and 4, respectively. 
     To determine the order number i of the desired component vector D i  in the diagonal direction, the identification accuracy in the learning step performed by the above-described identification unit can be used as a reference. That is, the order of the desired component vector can be determined so as to be the order (or a combination of the orders) that provides excellent identification accuracy in the learning step performed by the identification unit  13 . 
     Subsequently, the processing proceeds to step S 43  shown in  FIG. 5 . At step S 43 , an amount of change in movement of each of the feature distributions with time is computed. 
     To compute an amount of change in movement of a feature distribution, a method in which the median point of the feature distribution is computed first, and subsequently, a time difference between processing frames is computed can be used. The order C i  corresponding to the median point of each of the feature distributions is given by the following equation: 
     
       
         
           
             
               
                 
                   
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     In  FIGS. 9A ,  9 B, and  9 C, the orders corresponding to the median points of the feature distributions are indicated by arrows Ca, Cb, and Cc, respectively. 
     Subsequently, a time difference between processing frames is computed using the order corresponding to the median point of each of the feature distributions obtained by using equation (5). To compute the time difference, a method for computing a difference between sound parameters, which is widely used in speech recognition technology, can be used. Examples of the computational expression for computing the time difference are given as follows: 
     
       
         
           
             
               
                 
                   
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     In equations (6) and (7), Θ denotes the number of frames (a window width) for computing the time difference. The time difference computed using equation (6) or (7) is delivered to the identification unit  13  as a parameter. Thereafter, the above-described speech recognition process is performed. 
     A change in the order corresponding to the median point of each feature distribution over time and a change in the time subtraction of the order corresponding to the median point over time are described next. In examples illustrated in  FIGS. 10A to 10I  and  FIGS. 11A to 11I , a region Ra denotes an utterance “UN” that indicates an affirmative answer, a region Rn denotes an utterance “UUN” that indicates a negative answer, and a region Rq denotes an utterance “UN?” that indicates an interrogative. 
     In the example illustrated in  FIGS. 10A to 10I , three utterances “UN” that indicates an affirmative answer (the region Ra), utterance “UUN” that indicates a negative answer (the region Rn), and “UN?” that indicates an interrogative (the region Rq) are input using a normal voice (voice having pitch features) so that a variety of parameters are acquired. 
       FIG. 10B  illustrates speech wave data of the entire utterance.  FIG. 10A  is a diagram of part of the speech wave data shown in  FIG. 10B  enlarged along the time axis.  FIG. 10A  indicates that the input utterance is a sound having pitch features. 
       FIG. 10C  illustrates the spectrograms corresponding to the speech waves shown in  FIG. 10B .  FIG. 10C  also indicates that the input utterance is a sound having pitch features. 
       FIGS. 10D to 10F  illustrate changes over time in the orders corresponding to the median points of the above-described feature distributions.  FIGS. 10D ,  10 E, and  10 F correspond to the component vectors having the logarithmic frequencies of 2, 3, and 4, respectively. 
       FIGS. 10G ,  10 H, and  10 I illustrate changes over time in the time differences of the orders corresponding to the median points computed from  FIGS. 10D ,  10 E, and  10 F, respectively. The final identification parameters delivered to the identification unit  13  of  FIG. 1  are shown in  FIGS. 10G ,  10 H, and  10 I. 
     In  FIGS. 10G ,  10 H, and  10 I, the center point of the ordinate of the graph represents the origin of zero. A part having a positive time change in the order corresponding to the median point indicates that the pitch of the voice is increasing. In contrast, a part having a negative time change in the order corresponding to the median point indicates that the pitch of the voice is decreasing. As shown in  FIGS. 10G ,  10 H, and  10 I, the three types of utterances indicating an affirmative answer, a negative answer, and an interrogative exhibit different rising and falling of the voice pitch. 
     That is, as shown in  FIGS. 10G ,  10 H, and  10 I, in the utterance “UN” that indicates an affirmative answer (the region Ra), a falling part Rad primarily appears. In the utterance “UUN” that indicates a negative answer (the region Rn), a falling part Rnd appears first, and subsequently, a rising part Rnu appears. In the utterance “UN?” that indicates an interrogative (the region Rq), a rising part Rqu primarily appears. That is, in these graphs, the appearances of the rising part and falling part of the pitch of the voice are different. In this way, the differences among the utterances “UN” indicating an affirmative answer, “UUN” indicating a negative answer, and “UN?” indicating an interrogative can be distinguished. 
     Although, in  FIGS. 10G ,  10 H, and  10 I, changes over time in the time difference between the orders corresponding to the median points have substantially the same trend, the degrees of rising and falling are different due to the difference in the logarithmic frequency. 
     Note that, in the known method for detecting a pitch frequency, since the time difference between pitch frequencies is the same as the above-described time difference between the orders corresponding to the median points, the change over time is one parameter. However, according to the present embodiment, a plurality of time differences between the orders corresponding to the median points can be used by using desired component vectors. Accordingly, the changes in the features can be obtained in a variety of ways. Thus, a change in rising and falling of the pitch of voice that is difficult to detect in the known method can be reliably detected. 
       FIGS. 11A to 11I  illustrate the computational results acquired in the same manner as in  FIGS. 10A to 10I  when a quiet voice is input. Graphs in  FIGS. 11A to 11I  correspond to the graphs in  FIGS. 10A to 10I , respectively. 
     In  FIGS. 10A to 10I , the input voice is an ordinary utterance. In the voice area, the periodicity of the vibration of the vocal cords for sonant appears. In contrast, in  FIGS. 11A to 11I , a quiet voice is input as an input sound. In  FIGS. 11A to 11C , pitch features of sufficient size does not appear. Thus, it is very difficult to detect the pitch frequency. 
     However, as shown in  FIGS. 11G ,  11 H, and  11 I, even when a quiet voice is input, a rise and a fall of the pitch of the voice can be sufficiently detected by using the method according to the present embodiment. 
     That is, like the cases shown in  FIGS. 10G ,  10 H, and  10 I, in the cases shown in  FIGS. 11G ,  11 H, and  11 I, in the utterance “UN” that indicates an affirmative answer (the region Ra), a falling part Rad primarily appears. In the utterance “UUN” that indicates a negative answer (the region Rn), a falling part Rnd appears first, and subsequently, a rising part Rnu appears. In the utterance “UN?” that indicates an interrogative (the region Rq), a rising part Rqu primarily appears. 
     Accordingly, even when a quiet voice is input, the differences among the utterances “UN” indicating an affirmative answer, a negative answer “UUN”, and an interrogative “UN?” can be distinguished. In addition, even when noise is mixed with the utterance, a rise and a fall of the pitch of the voice can be sufficiently detected in the same manner. 
     In this way, the internal process of the sound analyzing unit  12  shown in  FIG. 12  is performed so that features used for identification are extracted. Thereafter, the features are delivered to the identification unit  13 . 
     When a parameter indicating the features is delivered from the sound analyzing unit  12  to the identification unit  13 , the time differences of the orders corresponding to the median points of the feature distributions at a given point of time t (a given analysis frame) are packed into one vector. More specifically, let a vector O t  denote the information output from the sound analyzing unit  12 . Then, the vector O t  is expressed as follows:
 
 O   t =( d   i(1)t   , d   i(2)t   , . . . , d   i(m)t   , . . . , d   i(M)t )   (8)
 
where i(m) (1≦m≦M) represents the order i of the entire feature distribution D={D i }, that is, the set of the desired component vectors D i  in the diagonal direction, and M represents the total number of the orders of the set {D i }.
 
     The identification unit  13  recognizes the unknown speech data using a parameter in the parameter storage unit  14 , which is generated on the basis of the feature obtained by sound-analyzing learning sound data in advance. The recognition process performed by the identification unit  13  is the same as that in a widely used speech recognition technology, and therefore, description is not provided here. 
     As described above, according to the present embodiment, the prosody identifying apparatus that identifies prosody using prosodic features of an input voice identifies prosody using an amount of change in movement of a feature distribution obtained from the frequency characteristic of the input voice. In this way, the prosody identifying apparatus can detect prosody that is difficult to be detected in the known methods using a robust approach. 
     Usage Together with Widely Used Sound Recognition Apparatus 
     Usage of the prosody identifying apparatus according to the present embodiment together with a widely used speech recognition apparatus is described next.  FIG. 12  illustrates a system configuration in which the prosody identifying apparatus according to the present embodiment is used together with a widely used speech recognition apparatus.  FIG. 13  is a flow chart of a process performed in the system configuration. 
     As shown in  FIG. 12 , a voice is input to an input unit  51 . The input unit  51  delivers the sound to a prosody identifying unit  52  according to the present embodiment and two processors of a widely used speech recognition unit  53 . Each of the prosody identifying unit  52  and the speech recognition unit  53  processes the input sound data. As a result of a prosody identification process, the prosody identifying unit  52  outputs an utterance type (or an utterance intension of a user) identified on the basis of a prosody pattern of the input sound data. In addition, as a result of a speech recognition process, the speech recognition unit  53  outputs text information corresponding to the input sound data. The results of a prosody identification process and a speech recognition process are then delivered to a result selection unit  54 . 
     The result selection unit  54  compares the result of a speech recognition process with each of specific words stored in a specific-word storage unit  55 . If the result of a speech recognition process matches one of the specific words, the result of a prosody identification process is attached to or integrated with the result of a speech recognition process. Subsequently, this integrated result is output from an output unit  56  of the system. However, if the result of a speech recognition process does not match any specific word, only the result of a speech recognition process is output from the output unit  56  of the system. 
     For example, when a specific word is “UN” and the prosody identifying unit  52  identifies whether a word is “UN” that indicates an affirmative answer, “UUN” that indicates a negative answer, or “UN?” that indicates an interrogative, information about the type indicating an affirmative answer, a negative answer, or an interrogative (the user&#39;s intension) is attached to the result of a speech recognition process. 
     Such an operation is described next with reference to a flow chart shown in  FIG. 13 . At step S 61 , a voice is input. At step S 62 , the input voice data is processed by each of the prosody identifying unit  52  and the speech recognition unit  53 , which are output the results of the processes. At step S 63 , it is determined whether the recognition result of the speech recognition unit  53  matches one of the above-described specific words. If it is determined to be NO (not match), the processing proceeds to step S 64 , where the recognition result of the speech recognition unit  53  is directly output. If, at step S 63 , it is determined to be YES (match), the processing proceeds to step S 65 , where the identification result of the prosody identifying unit  52  is attached to the recognition result of the speech recognition unit  53  and is output. 
     Alternatively, the result selection unit  54  may operate as follows. That is, the prosody identifying unit  52  identifies one of the following four utterance types: an “affirmative answer”, a “negative answer”, an “interrogative”, and “others” that indicates a type other than the preceding three types. In such a case, the need for the specific-word storage unit  55  that accompanies with the result selection unit  54  can be eliminated. If the result of a prosody identification process is “others”, only the result of a speech recognition process performed by the speech recognition unit  53  is output. If the result of a prosody identification process is one of the “affirmative answer”, “negative answer”, and “interrogative”, the result of a prosody identification process performed by the prosody identifying unit  52  is attached to the result of a speech recognition process performed by the speech recognition unit  53  and is output. 
     It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and alterations may occur depending on design requirements and other factors insofar as they are within the scope of the appended claims or the equivalents thereof.