Patent Publication Number: US-8538759-B2

Title: Speech recognition system and data updating method

Description:
CLAIM OF PRIORITY 
     The present application claims priority from Japanese patent applications JP 2009-010754 filed on Jan. 21, 2009 and JP 2009-086097 filed on Mar. 31, 2009, the contents of which are hereby incorporated by reference into this application. 
     BACKGROUND OF THE INVENTION 
     This invention relates to a speech recognition system, and more particularly, to a technology for providing, in the latest state, a user with recognition error patterns of a speech recognition system used in a navigation device. 
     A speech recognition system used in a car navigation device has an advantage of enabling operations on the device without constraining hands and eyes. Especially, car navigation devices offering a function of receiving a point of interest (POI) name uttered by a user as a speech input to thereby set a destination are in the market. 
     However, when a large number of words are registered in a dictionary of speech recognition held by the speech recognition system, sets of words causing recognition errors tend to occur, resulting in a lower possibility of recognition of a destination from an utterance of a user. To address this problem, JP 08-171396 A discloses a method in which tendencies of recognition errors among phonemes are obtained in advance, and, based on the tendencies, a result of performing error correction on an originally obtained recognition result based on the tendencies of recognition errors is added to a speech recognition result. Moreover, a method of, using tendencies of recognition errors, adding words other than words registered in a dictionary of speech recognition to a speech recognition result, is disclosed (see T. Fukada and Y. Sagisaka, “Automatic generation of a pronunciation dictionary based on a pronunciation network”, Proc. Eurospeech 1997, pp. 2471-2472, for example). 
     Further, JP 2006-317643 A discloses a technology of updating a map with a decreased data traffic by sending difference data of map data for a specific area from a navigation server (hereinafter, referred to as server) to a navigation terminal (hereinafter, referred to as terminal) via a network. 
     Still further, Shikano, et al., “Speech recognition system”, Ohmsha, 2001, describes a method of speech recognition. 
     SUMMARY OF THE INVENTION 
     When an update is carried out based on a difference of map data sent from the server to the terminal via the network, the difference of the map data almost always contains addition, update, and deletion of point of interest (POI) names. 
     When a POI name in the map data is changed, a word of the POI name in the dictionary of the speech recognition system is accordingly to be changed. Then, when the word of the speech recognition system is changed, a set of words having tendencies to cause recognition errors may be generated out of the changed words and existing words, and it is necessary to reexamine the tendencies of recognition errors. 
     Moreover, it is necessary to consider the following problems when the difference data is sent to the speech recognition system and the tendencies of recognition errors are acquired. 
     First, for acquiring the tendencies of recognition errors, due to the limited computer resources on the terminal, it is difficult to newly calculate the tendencies of recognition errors only by processing on the terminal. Moreover, when the tendencies of recognition errors are calculated only by the server and sent to respective terminals, the respective terminals hold different versions of a map, and contents of words registered in the recognition dictionary varies from terminal to terminal because the server cannot manage version information of the map of all the terminals. Therefore, it is difficult to acquire the tendencies of recognition errors only on the server. 
     Incidentally, the speech recognition system is known to carry out, in response to a speech input as a result of an utterance of a user, processing of outputting only one recognition result or processing of outputting a plurality of candidates. Moreover, to each candidate output by the speech recognition system, a score indicating a likelihood of the result is added. Even if a recognition result having the highest score is incorrect, by outputting a plurality of candidates, a possibility that the candidates include a correct answer increases. However, the speech recognition processing of outputting a plurality of candidates requires more computer resources, and the number of candidates to be output is limited when the processing is carried out only on the terminal. 
     This invention has been made to solve the problems described above, and therefore has an object to send difference data generated as a result of update of map data to a speech recognition system, to acquire tendencies of recognition errors, and to output a recognition result of higher accuracy to a user, and also to provide a mechanism which does not impose excessive loads on servers, terminals, and networks. 
     A representative aspect of this invention is as follows. That is, there is provided a speech recognition system installed in a terminal coupled to a server via a network. The terminal holds map data including a landmark. The speech recognition system manages recognition data including a word corresponding to a name of the landmark included in the held map data, and sends update area information indicating an area of the map data to be updated and updated time on the area indicated by the update area information to the server. The server generates, in a case of which recognition data of the area indicated by the update area information sent from the terminal has been changed after a time indicated by the updated time sent from the terminal, difference data between latest recognition data and recognition data corresponding to the area indicated by the update area information at a time indicated by the updated time and sends the generated difference data to the terminal along with map data on the area indicated by the update area information. The terminal updates the map data held in the terminal based on the map data sent from the server. The speech recognition system updates the recognition data managed by the terminal based on the difference data sent from the server. 
     According to this invention, when the map data has been changed, the dictionary in the terminal may be updated using limited computer resources (such as CPU and memory) and network resources. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention can be appreciated by the description which follows in conjunction with the following figures, wherein: 
         FIG. 1  is a system configuration diagram illustrating a relationship between car navigation terminals and a server according to a first embodiment of this invention; 
         FIG. 2  is a block diagram illustrating a configuration of a server according to the first embodiment of this invention; 
         FIGS. 3A to 3D  are explanatory diagrams illustrating examples of data included in a POI name data according to the first embodiment of this invention; 
         FIG. 4  is an explanatory diagram illustrating an example of a confusion score table according to the first embodiment of this invention; 
         FIG. 5  is a block diagram illustrating a configuration of a speech recognition system of a terminal according to the first embodiment of this invention; 
         FIG. 6  is an explanatory diagram illustrating a map held in the terminal, and versions thereof according to the first embodiment of this invention; 
         FIG. 7  is a flowchart illustrating processing performed by the terminal when map data is updated according to the first embodiment of this invention; 
         FIG. 8  is a flow chart illustrating processing performed by the server when the map data is updated according to the first embodiment of this invention; 
         FIG. 9A  is an explanatory diagram illustrating a table of POI name to delete included in difference data sent from the server to the terminal according to the first embodiment of this invention; 
         FIG. 9B  is an explanatory diagram illustrating a table of POI name to add included in the difference data sent from the server to the terminal according to the first embodiment of this invention; 
         FIG. 9C  is an explanatory diagram illustrating a table of information indicating POI names with changing confusion information according to the first embodiment of this invention; 
         FIGS. 10A to 10E  are explanatory diagrams illustrating examples of the POI name data according to the first embodiment of this invention; 
         FIG. 11  is an explanatory diagram illustrating an example of an interaction between a user and the terminal according to the first embodiment of this invention. 
         FIGS. 12A and 12B  are explanatory diagrams illustrating examples of the POI name data according to a second embodiment of this invention; and 
         FIG. 13  is a flowchart illustrating processing carried out by the terminal when the map data is updated according to the second embodiment of this invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Hereinbelow, a description is given of embodiments of this invention referring to the accompanying drawings. The descriptions of these embodiments are given of a case in which a dictionary of speech recognition relating to recognition of point of interest (POI) names is updated on a car navigation device. However, the embodiments can be applied to updates of other dictionaries than that of the POI names, the dictionaries being used for operations of the speech recognition for names of music pieces, addresses, intersections, and roads, and speech commands for operating devices. Moreover, a communication terminal is not limited to a car navigation device, and may be a portable navigation device (PND), a cellular phone, a PDA, and the like. 
     First Embodiment 
     System Overview (Server and Terminals) 
       FIG. 1  is a system configuration diagram illustrating a relationship between car navigation terminals and a server according to a first embodiment of this invention. 
     Each of a plurality of motor vehicles  11010  is provided with the car navigation terminal. A communication unit of the each motor vehicle  11010  communicates with a server  11030  via a network  11020 . 
     (Configuration of Server) 
       FIG. 2  is a block diagram illustrating a configuration of the server  11030  according to the first embodiment of this invention. 
     The server  11030  is a computer including a CPU, an output device, an input device, a network interface, a memory, and an auxiliary storage system, and communicates with the terminals on the motor vehicles  11010  via the network  11020 . In the memory, programs for realizing a speech analyzing unit  1012 , a speech recognizer  1040 , and a language model making unit  1060  are stored. Moreover, in the auxiliary storage system, speech data  1010 , an acoustic model storing unit  1014 , a language model storing unit  1041 , confusion data  1050 , and POI name data  1020  are stored. 
     In the speech data  1010 , speech data pieces of various uttered POI names are stored in advance. Moreover, in the speech data  1010 , speech data obtained when each of POIs uttered respectively by a plurality of persons is stored. This speech data may be speeches uttered by different speakers or speeches uttered by the same speaker a plurality of times. Moreover, speech data pieces sampled in different noise environments may be stored. 
     The speech analyzing unit  1012  converts data of a speech uttered by a user into a sequence of feature vectors. The sequence of feature vectors is obtained by representing, at respective time points, feature amounts of a speech delimited in terms of time as multidimensional vectors, and arranging them in a time sequence. For example, the sequence of feature vectors is obtained by delimiting input speech data into sections of several tens of milliseconds, converting the speech data at the respective time points into multidimensional vectors referred to as mel frequency cepstrum coefficient (MFCC) parameters, and arranging the vectors obtained through the conversion in a time sequence. 
     The acoustic model storing unit  1014  stores acoustic models corresponding to respective phonemes in advance. As a representation form of this acoustic model, the hidden Markov model (HMM) which defines each phoneme in three states may be employed. 
     The language model storing unit  1041  stores a dictionary  1042  and a grammar  1044 . The dictionary  1042  stores words to be recognized in the speech recognition, and phoneme strings of the words to be recognized. The grammar  1044  stores connection rules of the respective words stored in the dictionary  1042 . 
     The speech recognizer  1040  carries out speech recognition processing. Specifically, the speech recognizer  1040  searches an HMM network formed by connecting HMMs stored in the acoustic model storing unit  1014  according to the dictionary  1042  and connection rules of the grammar  1044  stored in the language model storing unit  1041  for a sequence of states in which a sequence of feature vectors of an input speech transferred from the speech analyzing unit  1012  is output at the highest probability. Further, the speech recognizer  1040  outputs a word sequence following the sequence of states obtained as a search result as a recognition result. Moreover, in addition to the word sequence, the speech recognizer  1040  calculates a score representing the likelihood of the word sequence as the recognition result. The speech recognition processing may be implemented by a method described in Shikano, et al., “Speech recognition system”, Ohmsha, 2001. The output word sequence and score are stored in the confusion data  1050 . On this occasion, a word sequence having the highest score or a plurality of word sequences having higher scores from the top may be output. 
     The POI name data  1020  contains data  1030  of POI names at various times and dates. 
       FIGS. 3A to 3D  are explanatory diagrams illustrating examples of the data  1030  included in the POI name data  1020  according to the first embodiment of this invention. 
     The POI name data  1020  contains data  1030  of POI names at a plurality of dates and times. This example includes a set of four versions ( FIGS. 3A to 3D ) of the data  1030  of the POI names in the chronological order. This set is managed according to version numbers. According to this embodiment, in an order from the oldest one, there are data  2010  of POI names of version  1  ( FIG. 3A ), data  2080  of POI names of version  2  ( FIG. 3B ), data  2090  of POI names of version  3  ( FIG. 3C ), and data  2100  of POI names of version  4  ( FIG. 3D ). In place of the version number, creation time and date of the data may be assigned as the management number. 
     The data  1030  of POI names of each version contains IDs  2030 , POI names  2040 , phoneme strings  2050 , areas  2060 , and latitudes/longitudes  2070 . The ID  2030  is a unique identifier for identifying a POI name. The phoneme string  2050  is data representing how the POI name  2040  is read. The area  2060  is data for identifying an area on a map containing the POI name. The latitude/longitude  2070  represents the position of this POI. It should be noted that, in addition to these records, a POI category, an address, a phone number, and a Alphabet string to be referred to when the name is input using a touch panel or remote controller, which are usually assigned to a POI in a car navigation device, may be added. Moreover, the area  2060  is not essential, and the area  2060  may be derived by predetermined calculation from the latitude/longitude  2070  if necessary. 
     Moreover, according to the first embodiment, both the server  11030  and the terminal use the POI name data  1020 . Though the POI name  2040  is used as an index key for search, the ID  2030  which is uniquely assigned to the POI name may be used as the index key. 
     Again, a description is given referring to  FIG. 2 . 
     The language model making unit  1060  converts the phoneme strings of the POI names stored in the POI name data  1020  into a language model in a form to be used for the speech recognition, and stores the language model obtained through the conversion in the language model storing unit  1041 . 
     The speech recognizer  1040  recognizes, based on the language model produced by the language model making unit  1060  from the phoneme strings  2050  of the POI names contained in the POI name data  1020 , various speech data pieces contained in the speech data  1010 , calculates tendencies of recognition errors of the respective speech data pieces, and updates the confusion data  1050  using the calculated recognition results. 
     Though there are a plurality of possible methods of calculating the tendency of a recognition error, a description is given of one of them as an example. 
     As the example, tendencies of recognition errors of speech data of a POI name “Asatte Hotel” and speech data of a POI name “Park Hotel” are to be obtained. In the following section, correct recognition of speech data uttered by a user by a speech recognition system as the user intended is referred to as correct answer, the POI name recognized in the event of the correct answer (“Asatte Hotel” in this example) is referred to as correct name, and the POI name incorrectly recognized with respect to the speech data (“Park Hotel” in this example) is referred to as error name. 
     It is assumed that there exist, in advance, N pieces of speech data obtained when users utter “Asatte Hotel”. The speech data may be speeches uttered by a plurality of persons, or speeches uttered by the same person a plurality of times. 
     Then, log probabilities of the respective speech data pieces with respect to the correct name “Asatte Hotel” and the error name “Park Hotel” are calculated. The log probability is obtained by calculating a logarithm of a probability that a sequence of feature vectors of a certain speech data X is generated from an acoustic model of a phoneme string forming a certain POI name W. As a method of this calculation, a method described in Shikano, et al., “Speech recognition system”, Ohmsha, 2001 may be employed. Moreover, the log probability of the speech data X with respect to the POI name W is represented as a notation P(X|W). 
     Then, a difference between a log probability of the each speech data obtained when the user utters “Asatte Hotel” with respect to the correct name “Asatte Hotel” and a log probability of this speech data with respect to the error name “Park Hotel” is obtained. A difference Di between the log probabilities calculated for the i-th speech data is obtained by the following equation.
 
 Di=P ( Xi|Wtgt )− P ( Xi|Wret )
 
where respective notations have the following meanings.
 
     i: number of speech data (i=1 to N) 
     Xi: i-th speech data 
     Wtgt: error name 
     Wref: correct name 
     When Di is positive, the log probability of the speech data with respect to the error name “Park Hotel” is higher than that with respect to the correct name “Asatte Hotel”. Thus, as the number of speech data pieces which give a positive Di is large, the speech data “Asatte Hotel” has a higher tendency to cause a recognition error as the error name “Park Hotel”. 
     Thus, a value obtained by dividing the number of the speech data pieces which give a positive Di by the number of all the speech data pieces is used to represent a tendency of occurrence of a recognition error. The tendency of occurrence of a recognition error is referred to as confusion score hereinafter. According to this method, the confusion score C(Ltgt|Lret) of an error name Ltgt with respect to the correct name Lref is represented by the following equation.
 
 C ( Ltgt|Lret )=( nDi&gt; 0) IN  
 
where nDi&gt;0 denotes the number of the speech data pieces which give the positive difference Di between the log probabilities among the N speech data pieces.
 
     For example, when there are 100 speech data pieces obtained when “Asatte Hotel” is uttered, and log probabilities of these speech data pieces are calculated with respect to both the POI names “Asatte Hotel” and “Park Hotel”, there are 30 speech data pieces having a higher log probability with respect to “Park Hotel” than that with respect to “Asatte Hotel”. In this case, the confusion score is calculated as 30/100=0.3. Moreover, using the same method, the confusion score of a POI name “Arc Hotel” is calculated as 0.2. 
     According to this method of calculating the tendency of a recognition error, as the confusion score is equal to less than 1 and closer to 1, the likelihood that an utterance of a user results in a recognition error becomes higher. Then, this confusion score is used to represent the likelihood of the recognition error. 
     The confusion data  1050  stores the confusion scores calculated by the speech recognizer  1040 . Referring to  FIG. 4 , a detailed description is given of this. 
       FIG. 4  is an explanatory diagram illustrating an example of a confusion score table  3010  according to the first embodiment of this invention. 
     The confusion score table  3010  contains POI names  3020 , versions  3030 , areas  3040 , and confusion information  3050 . The confusion information  3050  contains confusion POI names  3060 , versions-areas  3070 , and confusion scores  3080 . 
     The POI name  3020  is a name of a POI. 
     The version  3030  is a version of a map containing the POI name  3020 . For example, it is appreciated from the table  3010  that Tozai Byoin is contained in maps of versions  2 ,  3 , and  4 , but is not contained in a map of version  1 . 
     The area  3040  represents an area on the map containing the POI name  3020 . 
     The confusion information  3050  indicates POIs having a possibility of a recognition error as the POI indicated by the POI name  3020 . 
     The confusion POI name  3060  indicates a POI name having a possibility of a recognition error. 
     The version-area  3070  indicates correspondences between a version and an area of the map containing the confusion POI name  3060 . 
     The confusion score  3080  indicates a value representing a degree of tendency of the recognition error. According to the above-mentioned example of the calculation, the confusion score of the confusion POI name “Asatte Hotel” with respect to the POI name “Park Hotel” is 0.3. Moreover, the confusion score of the confusion POI name “Asatte Hotel” with respect to the POI name “Arc Hotel” is 0.2. 
     It should be noted that the method of calculating the confusion score described herein is merely an example. Any method may be employed as long as it can associate the tendency of a recognition error. For example, a tendency of a recognition error per phoneme may be obtained in advance, and a tendency of a recognition error per POI name may be calculated from the obtained tendency of a recognition errors per phoneme. Moreover, the speech recognition processing to be carried out by the speech recognizer  1040  may not be carried out on the server  11030 , but the confusion score may be determined based on a result obtained on another computer. Further, the confusion score may be determined manually. 
     Moreover, the confusion score may be changed even in the case of the same POI name for some reasons. In the example illustrated in  FIG. 4 , the confusion score of the confusion POI name “Kasai Byoin” with respect to the POI name “Tozai Byoin” is 0.7 for versions  1  to  3 , and is changed to 0.5 for version  4 . The information on a change in confusion score in this manner may be recorded in the table  3010 . 
     Moreover, speech data pieces in different noise states are stored in the speech data  1010 , and the confusion score may be calculated separately for the each noise state. Moreover, on each terminal, by using a confusion score in a noise state closest to an environment in which the terminal is used, a recognition result can be corrected by a more precise tendency of a recognition error. 
     (Configuration of Terminal) 
       FIG. 5  is a block diagram illustrating a configuration of a speech recognition system of the terminal according to the first embodiment of this invention. 
     The terminal is installed on the motor vehicle  11010 , includes a CPU, an output device, an input device, a network interface, a memory, and an auxiliary storage system, and communicates with the server  11030  via the network  11020 . 
     A speech input unit  10010  converts a speech signal received from a user into a digital signal, and acquires the digital signal as speech data. 
     A speech analyzing unit  10020  converts the speech data acquired by the speech input unit  10010  into a sequence of feature vectors. A method of the conversion to be used may be the same as that used by the speech analyzing unit  1012  of the server  11030 . 
     An acoustic model storing unit  10030  stores acoustic models corresponding to respective phonemes in advance. The method of storing the acoustic models to be used may be the same as that used by the acoustic model storing unit  1014  of the server  11030 . 
     A language model storing unit  10040  stores a dictionary  10042  and a grammar  10044 . 
     The dictionary  10042  stores words to be recognized in a speech recognizer  10050 , and phoneme strings thereof. According to the first embodiment, in the dictionary  10042 , words of POI names, and phoneme strings of the POI names are stored. Not all the POI names as the subject of the speech recognition may be stored in the dictionary  10042 , but only POI names restricted by a position of an own motor vehicle of a user, addresses and POI categories input in advance, and the like may be stored. 
     The grammar  10044  stores connection rules of the respective words stored in the dictionary  10042 . 
     The speech recognizer  10050  carries out speech recognition processing. Specifically, the speech recognizer  10050  searches an HMM network formed by connecting HMMs stored in the acoustic model storing unit  10030  according to the dictionary  10042  and connection rules of the grammar  10044  stored in the language model storing unit  10040  for a sequence of states in which a sequence of feature vectors of an input speech transferred from the speech analyzing unit  10020  is output at the highest probability. Further, the speech recognizer  10050  outputs a word sequence following the sequence of states obtained as a search result as a recognition result. Moreover, in addition to the word sequence, the speech recognizer  10050  calculates a score representing the likelihood of the word sequence as the recognition result. The speech recognition processing may be implemented by a method described in Shikano, et al., “Speech recognition system”, Ohmsha, 2001. On this occasion, a word sequence having the highest score or a plurality of word sequences having higher scores from the top may be output. 
     A recognition result storing unit  10052  temporarily stores results output by the speech recognizer  10050 . The speech recognizer  10050  may output results stored in the recognition result storing unit  10052  to a recognition result correcting unit  10060  or an output unit  10090 . 
     The recognition result correcting unit  10060  corrects the result output by the speech recognizer  10050  based on POI name data  10070 . The POI name data  10070  contains a list of POI names and confusion information on the POI names. Moreover, the POI name data  10070  contains phoneme strings representing how the POI names are read, and serves as a dictionary of speech recognition for the speech recognition system. Operations thereof are detailed later. 
     A language model making unit  10080  converts the phoneme strings of the POI names stored in the POI name data  10070  into a language model in a form to be used for the speech recognition, and stores the language model obtained through the conversion in the language model storing unit  10040 . Though this processing of the conversion into the language model is generally carried out when the processing of the speech recognition is not carried out, the processing of the conversion into the language model may be carried out when the speech recognition processing starts or while the recognition processing is being carried out. 
     The output unit  10090  outputs a speech recognition result sent from the recognition result correcting unit  10060  or the speech recognizer  10050  to functions such as a screen output and a sound output of the car navigation device. 
     (Structure of POI Name Data  10070 ) 
     A description is given of the POI name data  10070 . 
       FIG. 6  is an explanatory diagram illustrating a map  4010  held in the terminal, and versions thereof according to the first embodiment of this invention. 
     As illustrated in  FIG. 6 , the map  4010  held by the terminal is divided into areas in a mesh form, and contains four areas A to D. Moreover, versions of the areas A to D are respectively 3, 3, 2, and 3. Moreover, five POI names “Tozai Byoin”, “Hanatsu Onsen”, “Park Hotel”, “Asatte Hotel”, and “Kasai Byoin” illustrated in the map  4010  of  FIG. 6  are registered. The map and the version information held by the terminal are stored as digital data in the terminal. 
     In this state, in the POI name data  10070 , the information on the POI names loaded on the terminal and information on the POI name having a tendency to cause a recognition error by the terminal are stored. 
       FIGS. 10A to 10E  are explanatory diagrams illustrating examples of the POI name data  10070  according to the first embodiment of this invention. 
     The POI name data  10070  contains POI names  8060 , versions-areas  8070 , and confusion information  8080 . The confusion information  8080  contains confusion POI names  8090 , versions-areas  8100 , and confusion scores  8120 . 
     The POI name  8060  is a POI name stored in the terminal. 
     The version-area  8070  indicates a correspondence between an area containing the POI name  8060  and a version number of the map. For example, Tozai Byoin is present in the area A, and the version of the area A is 3, and thus, a relationship “3-A” is stored. 
     The confusion information  8080  indicates POI names having a possibility of a recognition error as the POI name  8060  by the speech recognition system of the terminal. 
     The confusion POI name  8090  indicates POI names having a possibility of a recognition error. 
     The version-area  8100  indicates correspondences between a version and an area of the map containing the confusion POI name  8090 . 
     The confusion score  8120  indicates a value representing a degree of tendency of the recognition error. For example, the Kasai. Byoin can be erroneously recognized as Tozai Byoin, and is assigned with 0.7 as the confusion score. 
     As a method of assigning the confusion scores, any of the methods including (1) initially assigning the confusion scores when the car navigation device is purchased, (2) assigning the confusion scores to a media such as a DVD and a CD used for updating the map, and (3) updating the confusion information when the map is updated based on a difference via the network may be employed. 
     To each POI name, a unique identifier of the POI, a phoneme string describing how to read the POI name, latitude and longitude of representing a position, a POI category, an address, and a phone number assigned to POIs generally on the car navigation device, and a Alphabet string referred to when the name is input using a touch panel or a remote controller, which are omitted in the POI name data  10070  according to this embodiment, may be assigned. 
     (Update of Confusion Information  8080 ) 
     A description is given of how to update the confusion information  8080  of the POI name data  10070  when the map held by the terminal is updated via the network  11020 . As an example on this occasion, the area D of the map  4010  of  FIG. 6  is updated from version  3  to version  4 , resulting in a map  4020 . Specifically, “Asatte Hotel” in the map  4010  is deleted, and “Tokai Byoin” and “Arc Hotel” are added. 
       FIG. 7  is a flowchart illustrating processing performed by the terminal when the map data is updated according to the first embodiment of this invention. 
     First, in Step  5010 , the terminal sends an update request of the map data to the server  11030 . A timing of sending this update request is a timing at which a user explicitly instructs the update or a timing at which the terminal automatically (periodically, for example) instructs the update. 
     Information  5012  sent from the terminal to the server  11030  contains an area for update, a version number of the map area for update currently used by the terminal, and the oldest version number of the map used by the terminal. The area for update is the area D, and the version of the map of the area D of the terminal is 3. Thus, “3” as the version number and “D” as the area for update are sent. Moreover, in the map  4010  illustrated in  FIG. 6 , the oldest map version is “2” of the area C, and thus, “2” is sent as the oldest version number. 
     When the server  11030  receives this information  5012 , the server  11030  starts the processing of the difference update. 
       FIG. 8  is a flow chart illustrating processing performed by the server  11030  when the map data is updated according to the first embodiment of this invention. Moreover,  FIG. 9A  is an explanatory diagram illustrating a table of POI name to delete  7010  included in the difference data sent from the server  11030  to the terminal according to the first embodiment of this invention,  FIG. 9B  is an explanatory diagram illustrating a table of POI name to add  7015  included in the difference data, and  FIG. 9C  is an explanatory diagram illustrating a table of information  7085  indicating POI names with changing confusion information ( 7085  is referred to as table of POI name with changing confusion information hereinafter), which is contained in the difference data. 
     The server  11030  receives, in Step  6010 , the update request sent in Step  5010 . 
     In Step  6020 , the server  11030  searches the POI name data  1020  to determine the number of the latest versions of the map of the area to which the update request is directed. As a result, it is found that version  4  is the latest version. Moreover, for the update of the area D from version  3  to version  4 , from data pieces registered in the server  11030 , the server  11030  extracts information on deletions, additions, and changes of POIs. This information on deletions, additions, and changes of POIs is referred to as the difference data. 
     A description is given of a method of making the difference data starting from Step  6030 . In Step  6030 , the server  11030  gets a list of POI names to be deleted from the area D by the update of the map data. The list of version  3  of the POI name data  1020  is illustrated in a table  2090  of  FIG. 3C , and the list of version  4  of the POI name data  1020  is illustrated in a table  2100  of  FIG. 3D . When POIs to be deleted from the area D are extracted by comparing the table  2090  and the table  2100  with each other, it becomes apparent that “Asatte Hotel” is to be deleted from the area D. Therefore, “Asatte Hotel” is extracted, and the extracted POI name is stored in a temporary area of the table of POI name to delete  7010  ( FIG. 9A ). 
     In Step  6040 , the server  11030  gets POI names to add to the area D by the update, and the confusion information on the POI names to add. When POIs to add to the area D are extracted by comparing the table  2090 , which is the list of version  3 , and the table  2100 , which is the list of version  4 , two POIs “Tokai Byoin” and “Arc Hotel” are extracted. 
     Moreover, the confusion information on “Tokai Byoin” and “Arc Hotel” is extracted from the table  3010 . The extracted confusion information is temporarily stored in the table of POI name to add  7015  ( FIG. 9B ). 
     In Step  6050 , from the information in the table of POI name to add  7015  extracted in Step  6040 , the server  11030  deletes information older than the oldest version held by the terminal. According to the information  5012  received in Step  6010 , the oldest version of the map held by the terminal is “2”. Thus, information older than the version number  2  (namely version  1 ) is no longer necessary. Thus, from the table of POI name to add  7015 , the information on POI names and confusion information relating to version  1  is deleted. Specifically, information on POIs contained only in version  1  out of the confusion information is deleted ( 7081 ). Moreover, out of version-area  7070 , information relating to version  1  is deleted ( 7082 ). 
     In Step  6060 , from information relating to the area for update (namely the area D) out of the information on the POI names to be added output in Step  6050 , information older than the version delivered to the terminal next time (namely version  4 ) is deleted. Specifically, a POI name which had been contained only up to version  3  in the area D is deleted ( 7083 ), and information up to version  3  in the area D out of the version-area  7070  is deleted ( 7084 ). 
     In Step  6070 , the server  11030  refers to the table  3010  and extracts confusion information that changes in all the areas as a result of the update of the POI names in the area for update (namely, due to the update from 3-D to 4-D). The extracted confusion information is temporarily stored in the table of POI name with changing confusion information  7085  ( FIG. 9C ). 
     A POI name  7090  is a POI name having confusion information to be changed due to the update of the map. It should be noted that confusion information of the POI names to be added have already been stored in the table of POI name to add  7015 , and have thus been removed from the table of POI name with changing confusion information  7085 . 
     A version  7100  and an area  7110  are respectively a version and an identifier of an area of the map containing the POI name  7090 . 
     Confusion information  7120  is confusion information to be changed due to the update. 
     A kind of update  7130  is a kind of the update indicated by the updated data. Specifically, there are three kinds: add, update, and delete. 
     A confusion POI name  7140  is a POI name which the speech recognition system tends to cause a recognition error. 
     A version-area  7150  indicates a correspondence between a version and an area of a map containing the confusion POI name  7140 . 
     A confusion score  7160  is a confusion score of the confusion POI name  7140 . 
     In Step  6073 , out of the information output in Step  6070 , the server  11030  deletes confusion information older than the oldest map version held by the terminal. The oldest map version in the terminal is “2”, and information on the POI name “Tozai clinic” present only in versions (namely version  1 ) older than the oldest map version is deleted ( 7170 ). 
     In Step  6076 , from information relating to the area for update (namely the area D) out of the information output in Step  6073 , information older than the version delivered to the terminal next time (namely version  4 ) is deleted. Specifically, the version of the area for update D is “4”, and thus, information on “Asatte Hotel” contained only up to version  3  in the area D is deleted ( 7180 ). 
     In Step  6080 , updated data to be sent to the terminal is made. The updated data to be sent includes the table of POI name to delete  7010 , the table of POI name to add  7015 , and the table of POI name with changing confusion information  7085 . 
     To each of the POI names contained in the updated data, a unique identifier of the POI may be added, which is omitted herein. Moreover, the table of POI name to add  7015  may also include a phoneme string describing how to read the POI name, latitude and longitude representing a position, a POI category generally assigned to the POI on the car navigation device, an address, a phone number, and a Alphabet string referred to when the name is input using a touch panel or a remote controller. 
     In Step  6090 , the extracted updated data and the map data for which the update request is received in Step  6010  are sent to the terminal. 
     Referring again to  FIG. 7 , a description is given of processing, on the terminal, of the updated data sent from the server  11030 . 
     In Step  5020 , the terminal receives the updated data sent from the server  11030 . 
     In Step  5030 , the terminal refers to the received updated data, and determines whether there are POI names to delete from the POI name data  10070  of the terminal. In this example, the table of POI name to delete  7010  contains “Asatte Hotel”, and thus the terminal proceeds to Step  5040 . 
     In Step  5040 , the terminal deletes the POI name  8060  to delete from the POI name data  10070 . Referring to  FIGS. 10A to 10D , this operation is described. 
     A table  8010  in  FIG. 10A  illustrates the POI name data  10070  before the update. 
     From the POI name data  10070 , “Asatte Hotel” contained in the POI names  8060  is searched for, and is deleted ( 8022 ). 
     In Step  5050 , the terminal searches the confusion POI names  8090  contained in the confusion information  8080  for the POI name  8060  to delete, and deletes the POI name  8060  ( 8024 ). As a result, the POI name data  10070  is now represented by a table  8020  illustrated in  FIG. 10B . 
     In Step  5060 , the terminal refers to the updated data, and determines whether there are POI names to add to the POI name data  10070  of the terminal. In this example, the table of POI name to add  7015  exists, and thus the terminal proceeds to Step  5070 . 
     In Step  5070 , the terminal adds the POI names  7020  to add to the POI name data  10070 . There are two POI names  7020  to add: “Tokai Byoin” and “Arc Hotel”, and thus, they are added ( 8032 ). On this occasion, the POI names to add are preferably added also to the dictionary  10042 . 
     Moreover, on this occasion, the version of the POI names which are contained in the area D, and are not to be changed is updated ( 8034 ). As a result, the POI name data  10070  is now represented by a table  8030  illustrated in  FIG. 10C . 
     In Step  5080 , the terminal deletes, from the confusion information  7050  assigned to the table of POI name to add  7015 , information that has no relationship with any POI name data in the terminal ( 8043 ,  8046 ). As a result, the POI name data  10070  is now represented by a table  8040  illustrated in  FIG. 10D . 
     In Step  5090 , the terminal determines whether there is confusion information  8080  to be changed out of the confusion information assigned to the existing POI names  8060 . In this example, the table of POI name with changing confusion information  7085  exists. Thus, the terminal proceeds to Step  5100 . 
     In Step  5100 , based on the table of POI name with changing confusion information  7085  sent from the server  11030 , the terminal changes the POI name data  10070 . For example, the table of POI name with changing confusion information  7085  contains data indicating a change by adding the confusion POI name “Tokai Byoin” to the POI name “Tozai Byoin”, and thus, this confusion POI name is added to the POI name data  10070 . Moreover, there is also data indicating a change to the confusion score  8120  of the POI name “Kasai Byoin”, and, based on this data, the confusion, score  8120  of the POI name data  10070  is changed. Moreover, the data sent from the server  11030  contains the POI names  7090  which are not held by the terminal. These POI names  7090  are not added in Step  5100 . Specifically, though “Tohachi Byoin” is contained in version  4  of the area A, the area A is version  3  on the terminal, and thus this POI name is not added to the POI name data  10070  (delete  8056 ). As a result, the POI name data  10070  is now represented by a table  8050  illustrated in  FIG. 10E . 
     In Step  5110 , the terminal updates the speech recognition language model  10040  of the terminal so as to recognize the new POI names. Specifically, the terminal uses phoneme strings to which pronunciations of the POI names  8060  of the POI name data  10070  are registered, to thereby make a language model in a form to be read by the speech recognizer  10050 . 
     (Description of Interaction/Operation of Recognition Result Correcting Unit) 
     Next, a description is given of a correction of a recognition result based on the confusion information when a user actually inputs a POI name. 
       FIG. 11  describes an example of an interaction between a user and the terminal according to the embodiment of this invention. 
     The user utters, as an instruction  9010 , “Tozai Byoin” to the terminal. 
     In Step  9020 , first, the terminal carries out the speech recognition in response to the user utterance. For the speech recognition, the speech recognition system illustrated in  FIG. 5  is used, and a word sequence as a recognition result and a score representing a likelihood thereof are output. As a result, the speech recognizer  10050  makes a recognition error, and outputs a result “Tokai Byoin” at a score 1.0. This result is stored in the recognition result storing unit  10052 . Moreover, this result is sent to the output unit  10090 . The output unit  10090  sends the result to a screen, a speech synthesizer, and other modules of the terminal. As a result, the terminal returns a response “Tokai Byoin, is that right?” to the user ( 9030 ). 
     In response to this question, it is assumed that the user utters “No” because an unintended result has been returned (instruction  9032 ). This speech is recognized by the speech recognition system of  FIG. 5 , and the terminal recognizes that the result is not accepted. 
     Then, the speech recognizer  10050  extracts the previous recognition result “Tokai Byoin” from the recognition result storing unit  10052 , and sends the result to the recognition result correcting unit  10060 . 
     The recognition result correcting unit  10060  refers to the POI name data  10070  stored in the terminal, thereby referring to confusion information corresponding to the name “Tokai Byoin”. On this occasion, it is assumed that the POI name data  10070  is in the state of the table  8050  illustrated in  FIG. 10E . Referring to the table  8050 , “Tozai Byoin” and “Kasai Byoin” are corresponding confusion POI names  8090 . Moreover, their confusion scores  8120  are respectively 0.6 and 0.5. 
     Based on this result, the recognition result correcting unit  10060  adds “Tozai Byoin” and “Kasai Byoin” to the original recognition result “Tokai Byoin” as candidates of the recognition result. Moreover, the scores of the “Tozai Byoin” and the “Kasai Byoin” are corrected respectively to 0.6 and 0.5 by multiplying the score 1.0 of the original recognition result “Tokai Byoin” by the respective confusion scores  8120 . The recognition result correcting unit  10060  sends these three POI names to the output unit  10090 . The output unit  10090  sends the result to a screen, a speech synthesizer, and other modules of the terminal. Then, the terminal outputs a response “There are three candidates. 1 Tokai Byoin, 2 Tozai Byoin, 3 Kasai Byoin” to the user (response  9050 ). 
     In response to this output, when a sound “Two” is input from the user (instruction  9052 ), the terminal selects the second “Tozai Byoin” (Step  9060 ), and sends the selection to the screen, the speech synthesizer, and other modules of the terminal (response  9070 ). As a result, the user may input the desired POI name. 
     Hereinabove, the flow of the steps according to the first embodiment has been described, but the method of interaction between the user and the terminal is not limited to the method according to the first embodiment. In an alternative method, the speech recognition result step by the terminal ( 9020 ) may be immediately sent to the recognition result correcting unit  10060  to thereby increase the number of candidates. In this case, there may be adopted a configuration in which the recognition result storing unit  10052  is removed from the speech recognition system of the terminal. 
     Moreover, as long as the tendency of recognition error is employed, the method is not limited to the method according to the first embodiment. For example, as the confusion information, a score is calculated when speech data of an uttered certain POI name is recognized by using a dictionary of a correct POI name, a score is calculated when the speech data is recognized by using a dictionary of a different POI name, and a mean difference of the calculated scores is directly used as the confusion information. Moreover, according to the mean difference, a method of correcting a score of a recognition result, or increasing the number of words output as a result may be employed. Moreover, this can be applied to various methods in which confusion information is added when the difference update is carried out. 
     Moreover, how to use the confusion score or the score output by the speech recognizer is not specifically described in the first embodiment, but this can be applied to various control methods based on an interaction employing the score of the recognition result. 
     In the example of the interaction described in the first embodiment, in the response  9050 , all the candidates are read as output. Another possible method is to refer, when the user utters “No”, to a POI name having the next highest score, thereby asking the user whether this POI name is an intended POI name. In other words, when the user utters “No”, the terminal refers to Tozai Byoin having the next highest score, thereby generating a response “Tozai Byoin, is that right?”. Further, when the user utters “No”, the terminal uses Kasai Byoin having the next highest score, thereby generating a response “Kasai Byoin, is that right?”. 
     As a still alternative method, out of a plurality of POI name candidates obtained according to the confusion information, based on history of use of the motor vehicle by the user, a POI name which the user selects with the highest probability may be output to the user. 
     According to the first embodiment of this invention, when the map data has been changed, the dictionary on the terminal can be updated using limited computer resources (such as a CPU and a memory) and resources of the network. Moreover, the terminal obtains the tendencies of recognition errors, and thus, a result desired by a user can quickly be obtained from the speech recognition system. 
     In the difference update of the dictionary, similarly to an ordinary update of a map, a method of only adding and deleting words in the dictionary is conceivable. However, a speech uttered by a user who wants to go to “Tozai Byoin” may be erroneously recognized as “Tokai Byoin” which is a POI name added as a result of an update. Moreover, conversely, even when a user utters a POI name which is newly added, the utterance may be erroneously recognized as another existing POI name. 
     In order to address this situation, if the terminal had a high computer processing performance, the speech recognizer could output a plurality of results, and could output, as a candidate, the correct name “Tozai Byoin”. However, the terminal has the limited computer resources, and the number of candidates to be output is thus limited. 
     Even in this case, according to the method described in the first embodiment, the correct name can be included in candidates. 
     Moreover, when the confusion information is downloaded in the difference update, it is difficult to download the confusion information on all the POIs due to the enormous data traffic. According to the first embodiment, by downloading the confusion information only on the POIs to be updated, the data traffic can be reduced. Moreover, on the terminal, by also updating the confusion information on existing POI names having the tendencies of recognition errors influenced by the POIs to be updated, it is possible for the terminal to obtain more precise tendencies of recognition errors. 
     Second Embodiment 
     Difference Data excluding Confusion Scores of Words which cause Recognition Errors 
     According to a second embodiment, assumptions of the update are the same as those of the first embodiment. The second embodiment is different from the first embodiment in two points: (1) the table of POI name with changing confusion information ( 7085 ) illustrated in  FIG. 9C  among the confusion information sent from the server  11030  is not sent; and (2) the processing in Step  5100  performed by the terminal illustrated in  FIG. 7  is different. In this embodiment, in Steps  5090  and  5100  in which the confusion information on the POI names is updated on the terminal, the confusion information on the added POI names is used. 
       FIGS. 12A and 12B  are explanatory diagrams illustrating examples of the POI name data  10070  according to the second embodiment of this invention.  FIG. 13  is a flowchart illustrating processing carried out by the terminal when the map data is updated according to the second embodiment of this invention. 
     Steps  13010  to  13080  of  FIG. 13  are the same as Steps  5010  to  5080  of  FIG. 7 , and hence description thereof is omitted. 
     Changes of the POI name data as a result of Steps  13090  and  13100  are illustrated in  FIGS. 12A and 12B . A table  12010  illustrated in  FIG. 12A  is the same as the table  8040  of the POI name data in midstream of the update illustrated in  FIG. 10D . It should be noted that the deletion  8043  and the deletion  8046  of the confusion information represented by strikethroughs in the table  8040  have already been removed from the table  12010 . 
     Referring to the confusion POI names of the added POI names “Tokai Byoin” and “Arc Hotel”, “Tozai Byoin” and “Kasai Byoin” are described as confusion POI names of “Tokai Byoin”, and “Park Hotel” is described as a confusion POI name of “Arc Hotel”. Confusion scores are assigned respectively thereto. 
     Then, combinations which are the inverses of the correspondences between the added POI name and the confusion POI name, namely, existing POI name is same as confusion POI name of the added POI name, are searched for, and when such POI names are found (Step  13090 ), to the confusion information of the existing POI names, the added confusion POI names are added as confusion POI names and their corresponding confusion scores are added, or the confusion scores of the confusion information of the existing POI names are updated (Step  13100 ). The POI name data after the update is illustrated in a table  12020  ( FIG. 12B ). For example, a POI name “Arc Hotel” has a confusion POI name “Park Hotel”, and the confusion score is 0.2. Based on this correspondence, a POI name “Park Hotel” is searched for, and, as confusion information thereof, a confusion POI name “Arc Hotel” is added, and the confusion score of “Arc Hotel” is set to 0.2. In the same way, based on the added POI name “Tokai Byoin”, confusion information is added by the same processing. 
     When Step  13100  is completed or there are no POI names meeting the condition in Step  13090 , the processing proceeds to Step  13110 . Step  13110  is the same as Step  5110  of  FIG. 7 , and the subsequent steps are the same as those of the first embodiment. Hence, a detailed description thereof is omitted. 
     The second embodiment employs the method in which the server does not send the confusion information on existing POI names to the terminal. In this case, based on an empirical rule that, generally, when a speech of a word A is erroneously recognized as a word B, a speech of the word B may be erroneously recognized as the word A, when a correspondence between a POI name and a confusion POI name is same, the same confusion score can be assigned to a confusion POI name in a correspondence in which the POI name appears as the confusion POI name and the confusion POI name appears as a POI name. 
     Thus, the server does not send the confusion information on existing POI names to the terminal, the data traffic is further reduced, use of resources can be reduced, and the confusion information can be efficiently updated. 
     Moreover, the description of the second embodiment is given of the case in which, based on the table of POI name to add sent from the server, the confusion information pieces of the existing POI names are updated. However, the existing POI names may be updated previously. In this case, the original confusion information may not be overwritten, and may be maintained. 
     (Applications to Configurations without Network) 
     The descriptions of the first and second embodiments are given of the case in which the server and the terminal have configurations independently of each other. 
     However, it is not essential to separate the server and the terminal, and the configurations of  FIGS. 2 and 5  may be provided on the same terminal. 
     Moreover, when the confusion information is updated, the communication over the network may not be employed. For example, in the first and second embodiments, in place of the update using the information downloaded from the server through the communication, information recorded on a disk medium such as a DVD may used for the update. The data on this medium may be input to the auxiliary storage system via the input device of the terminal, thereby reading out the same information as that downloaded from the server in the first and second embodiments from the DVD, and updating the POI name data containing the confusion information, and the language model. 
     (Applications to Update of Data Other than Map) 
     Moreover, the descriptions of the first and second embodiments are given of the update of map data. However, this invention can be applied to data other than the map, which may be partially updated. For example, the POI name data in a state without the latitude and longitude may be used as a simple POI name list. Moreover, this invention may be applied to data of music pieces which can be listened to on a car audio device. 
     While the present invention has been described in detail and pictorially in the accompanying drawings, the present invention is not limited to such detail but covers various obvious modifications and equivalent arrangements, which fall within the purview of the appended claims.