Abstract: An operator recognition device is provided that eliminates the registration of data such as HMM data having a characteristic amount for which error in recognition occurs easily when recognizing an operator, and thus reduces the possibility of errors in recognition, and has stable recognition performance. When registering HMM data that is used when performing recognition processing, a speaker recognition device 100 eliminates the registration of HMM data of a password having a characteristic amount of the spoken voice component that is similar to a characteristic amount that is indicated by HMM data that is already registered, and does not allow the registration of HMM data for which it is estimated that error in recognition will occur easily during the recognition process.

Abstract: There is provided a voice recognition device and a voice recognition method that enhance the function of noise adaptation processing in voice recognition processing and reduce the capacity of a memory being used. Acoustic models are subjected to clustering processing to calculate the centroid of each cluster and the differential vector between the centroid and each model, model composition between each kind of assumed noise model and the calculated centroid is carried out, and the centroid of each composition model and the differential vector are stored in a memory. In the actual recognition processing, the centroid optimal to the environment estimated by the utterance environmental estimation is extracted from the memory, model restoration is carried out on the extracted centroid by using the differential vector stored in the memory, and noise adaptation processing is executed on the basis of the restored model.

Abstract: An acoustic model registration apparatus, an talker recognition apparatus, an acoustic model registration method and an acoustic model registration processing program, each of which prevents certainly an acoustic model having a low recognition capability for talker from being registered certainly, are provided.

Abstract: An operator recognition device is provided that eliminates the registration of data such as HMM data having a characteristic amount for which error in recognition occurs easily when recognizing an operator, and thus reduces the possibility of errors in recognition, and has stable recognition performance. When registering HMM data that is used when performing recognition processing, a speaker recognition device 100 eliminates the registration of HMM data of a password having a characteristic amount of the spoken voice component that is similar to a characteristic amount that is indicated by HMM data that is already registered, and does not allow the registration of HMM data for which it is estimated that error in recognition will occur easily during the recognition process.

Abstract: EN) A speaker recognition system (1) includes a speaker model registration device (10) which registers a speaker model for speaker recognition in the speaker recognition system. The speaker model registration device includes acquisition means (13) for acquiring utterances by n+? times (wherein n is an integer not smaller than 2 and ? is an integer not smaller than 1); calculation means (20) for calculating a speaker model by using the acquired utterances of n times as utterances for registration; correlation means (30) for correlating the calculated speaker model by using the acquired utterances of ? times as correlation utterances; and registration means (40) for registering those having the correlation result satisfying a predetermined reference among the correlated speaker models, as the speaker model for speaker recognition.

Abstract: There is provided a voice recognition device and a voice recognition method that enhance the function of noise adaptation processing in voice recognition processing and reduce the capacity of a memory being used. Acoustic models are subjected to clustering processing to calculate the centroid of each cluster and the differential vector between the centroid and each model, model composition between each kind of assumed noise model and the calculated centroid is carried out, and the centroid of each composition model and the differential vector are stored in a memory. In the actual recognition processing, the centroid optimal to the environment estimated by the utterance environmental estimation is extracted from the memory, model restoration is carried out on the extracted centroid by using the differential vector stored in the memory, and noise adaptation processing is executed on the basis of the restored model.

Abstract: The present invention is directed to provide a data selecting apparatus and a navigation apparatus capable of easily and promptly selecting one piece of data from a plurality of pieces of data. A navigation apparatus 100 has: a display controller 111 for obtaining name data and genre information of each point data from a map data storing unit 105, generating display data for displaying names of the point data, which are arranged by the genre information at the same hierarchical level on the basis of the obtained name data and genre information, and performing the display control on the generated display data, and an operating unit 106 used for selecting a genre to which point data to be selected by the user belongs and selecting a name of one piece of the point data from the selected genre.

Abstract: A speech recognition apparatus and speech recognition method are provided for reducing such events as erroneous recognition and disabled recognition and improving a recognition efficiency.

Abstract: Before executing a speech recognition, a composite acoustic model adapted to noise is generated by composition of a noise adaptive representative acoustic model generated by noise-adaptation of each representative acoustic model and difference models stored in advance in a storing section, respectively. Then, the noise and speaker adaptive acoustic model is generated by executing speaker-adaptation to the composite acoustic model with the feature vector series of uttered speech. The renewal difference model is generated by the difference between the noise and speaker adaptive acoustic model and the noise adaptive representative acoustic model, to replace the difference model stored in the storing section therewith. The speech recognition is performed by comparing the feature vector series of the uttered speech to be recognized with the composite acoustic model adapted to noise and speaker generated by the composition of the noise adaptive representative acoustic model and the renewal difference model.

Abstract: A contents presenting system includes: an analyzing unit which collects and analyzes user's conversation to output an analysis result; a contents acquiring unit which acquires contents from a contents database based on the analysis result; and a contents presenting unit which presents the acquired contents to the user. Since the analysis result of user's conversation includes a factor representing the environment where the user is talking, by determining contents based on the analysis result, it is possible to provide the contents which is suited for the environment where the user is present.

Abstract: A speech synthesizing method which synthesizes speech naturally is disclosed. Standardized frame power values of an n-th frame is calculated when frame power values at head and tail frames in a phoneme are standardized. An average value of the power values sampled from the power frequency characteristics in the n-th frame at a predetermined frequency interval is set as a mean frame power value. A sum of squares of signal levels in one frame of a frequency signal from a sound source is calculated as a frame power correction value. A speech envelope signal is calculated as a function having variables of the standardized frame power values, the frame power correction value and the mean frame power value. The speech envelope signal adjusts the amplitude level of a speech waveform signal supplied from a vocal tract filter according to the level of the speech envelope signal.

Abstract: At the time of the speaker adaptation, first feature vector generation sections (7, 8, 9) generate a feature vector series [Ci, M] from which the additive noise and multiplicative noise are removed. A second feature vector generation section (12) generates a feature vector series [Si, M] including the features of the additive noise and multiplicative noise. A path search section (10) conducts a path search by comparing the feature vector series [Ci, m] to the standard vector [an, m] of the standard voice HMM (300). When the speaker adaptation section (11) conducts correlation operation on an average feature vector [S^n, m] of the standard vector [an, m] corresponding to the path search result Dv and the feature vector series [Si, m], the adaptive vector [xn, m] is generated. The adaptive vector [xn, m] updates the feature vector of the speaker adaptive acoustic model (400) used for the speech recognition.

Abstract: An initial combination HMM 16 is generated from a voice HMM 10 having multiplicative distortions and an initial noise HMM of additive noise, and at the same time, a Jacobian matrix J is calculated by a Jacobian matrix calculating section 19. Noise variation Namh (cep), in which an estimated value Ha^(cep) of the multiplicative distortions that are obtained from voice that is actually uttered, additive noise Na(cep) that is obtained in a non-utterance period, and additive noise Nm(cep) of the initial noise HMM 17 are combined, is multiplied by a Jacobian matrix, wherein the result of the multiplication and initial combination HMM 16 are combined, and an adaptive HMM 26 is generated. Thereby, an adaptive HMM 26 that is matched to the observation value series RNah(cep) generated from actual utterance voice can be generated in advance.

Abstract: A multiplicative distortion Hm(cep) is subtracted from a voice HMM 5, a multiplicative distortion Ha(cep) of the uttered voice is subtracted from a noise HMM 6 formed by HMM, and the subtraction results Sm(cep) and {Nm(cep)?Ha (cep)} are combined with each other to thereby form a combined HMM 18 in the cepstrum domain. A cepstrum R^a(cep) obtained by subtracting the multiplicative distortion Ha (cep) from the cepstrum Ra (cep) of the uttered voice is compared with the distribution R^m(cep) of the combined HMM 18 in the cepstrum domain, and the combined HMM with the maximum likelihood is output as the voice recognition result.

Abstract: A trained vector creating part 15 creates a characteristic of an unvoiced sound in advance as a trained vector V. Meanwhile, a threshold value THD for distinguishing a voice from a background sound is created based on a predictive residual power ? of a sound which is created during a non-voice period. As a voice is actually uttered, an inner product computation part 18 calculates an inner product of a feature vector A of an input signal Sa and a trained vector V, and a first threshold value judging part 19 judges that it is a voice section when the inner product has a value which is equal to or larger than a predetermined value ? while a second threshold value judging part 21 judges that it is a voice section when the predictive residual power ? of the input signal Sa is larger than a threshold value THD.

Abstract: A system and method include an initial noise model produced based on pre-estimated noise of a service environment and an initial synthesized model of a voice containing noise. The system and method produce an utterance environment noise model from background noise of the service environment upon speech recognition as well as a sequence of feature vectors from noise-superimposed speech including an uttered voice and the background noise. The system and method also produce an adaptive model by adapting the initial synthesized model using the utterance environment noise model, the initial noise model, and a compensation model, so that the adaptive model is checked against the sequence of feature vectors to perform speech recognition. Upon performing the speech recognition, a compensation model is created upon which the signal to noise ratio between the background noise present at the time of actual utterance of a voice and the uttered voice is reflected.

Abstract: An apparatus for recognizing a word based on voice information, includes, a keyword model storing device, a non-keyword model storing device, a model updating device and a recognition device. The keyword model storing device stores words to be potentially spoken, as keyword models. The non-keyword model storing device stores words to be potentially spoken, as non-keyword models. The model updating device updates the recognition and non-keyword models, based on a previously recognized word. The recognition device matches the recognition and non-keyword models updated with the voice information. The model updating device updates the non-keyword models utilizing a non-keyword variation vector, which is indicative of variation of the non-keyword models, from the non-updated to the updated, and has been set to be smaller than a non-keyword variation vector applied in the updating of the non-keyword models.

Abstract: Before executing a speech recognition, a composite acoustic model adapted to noise is generated by composition of a noise adaptive representative acoustic model generated by noise-adaptation of each representative acoustic model and difference models stored in advance in a storing section, respectively. Then, the noise and speaker adaptive acoustic model is generated by executing speaker-adaptation to the composite acoustic model with the feature vector series of uttered speech. The renewal difference model is generated by the difference between the noise and speaker adaptive acoustic model and the noise adaptive representative acoustic model, to replace the difference model stored in the storing section therewith. The speech recognition is performed by comparing the feature vector series of the uttered speech to be recognized with the composite acoustic model adapted to noise and speaker generated by the composition of the noise adaptive representative acoustic model and the renewal difference model.

Abstract: A true/false judgment on a result of speech recognition is made with high accuracy using a less volume of processing. By comparing acoustic models HMMsb against the feature vector sequence V(n) of utterances, a recognition result RCG specifying the acoustic model HMMsb having the maximum likelihood, a first score FSCR indicting the value of the maximum likelihood, and a second score SSCR indicating the value of the second highest likelihood are found. Then, by comparing an evaluation value FSCR×(FSCR−SSCR) based on the first score FSCR and the second score SSCR with a pre-set threshold value THD, a true/false judgment on the recognition result RCG is made. When the recognition result RCG is judged as being true, speaker adaptation is applied to the acoustic models HMMsb, and when the recognition result RCG is judged as being false, speaker adaptation is not applied to the acoustic models HMMsb. It is thus possible to improve the accuracy of speaker adaptation.

Abstract: A speech recognition device comprises an HMM model database which prestores keyword HMMs which represent feature patterns of keywords to be recognized, likelihood calculator which calculates the likelihood of an extracted feature value of a speech signal in each frame by comparing it with keyword HMMs and designated-speech HMMs, extraneous-speech likelihood setting device which sets extraneous-speech likelihood based on the calculated likelihood of a match with the designated-speech HMMs, matching processor which performs a matching process based on the calculated likelihood and the extraneous-speech likelihood, and determining device which determines the keywords contained in the spontaneous speech based on the matching process.