Abstract: A speech signal processing device is equipped with a power acquisition unit, a probability distribution acquisition unit, and a correspondence degree determination unit. The power acquisition unit accepts an inputted speech signal and, based on the accepted speech signal, acquires power representing the intensity of a speech sound represented by the speech signal. The probability distribution acquisition unit acquires a probability distribution using the intensity of the power acquired by the power acquisition unit as a random variable. The correspondence degree determination unit determines whether a correspondence degree representing a degree that power acquired by the power acquisition unit in a case that a predetermined reference speech signal is inputted into the power acquisition unit corresponds with predetermined reference power is higher than a predetermined reference correspondence degree, based on the probability distribution acquired by the probability distribution acquisition unit.

Abstract: A signal correction apparatus receives an input audio signal (serving as a first sound reception means). The signal correction apparatus computes, at every frequency, first power that indicates magnitude of sound represented by the input audio signal (serving as a first power computation means). The signal correction apparatus estimates a correction function that is a continuous function defining a relation between each frequency and a correction coefficient used to approximate the first power computed at that frequency to the reference power predetermined for that frequency (serving as a correction function estimation means). The signal correction apparatus multiplies the computed first power by the correction coefficient acquired in accordance with the relation defined by the estimated correction function so as to correct the first power at every frequency (serving as a power correcting means).

Abstract: A speech sound detection apparatus receives an input audio signal (as a sound reception unit), and computes input power that indicates a magnitude of the sound represented by the audio signal (as an input power computation unit). The apparatus estimates a correction function that is a continuous function defining a relation between a certain frequency and a correction coefficient used to approximate the input power computed at that frequency to the reference power predetermined for that frequency (as a correction function estimation unit). The apparatus corrects the input power at every frequency, based upon the correction coefficient that is obtained in accordance with the relation defined by the estimated correction function (as an input power correcting unit). The apparatus further determines whether or not the sound represented by the received audio signal is speech sound, based upon the corrected input power (as a speech sound detection unit).

Abstract: To this end, a voice detection device includes a band-based power calculation unit that calculates a total of signal power values (sub-band power) of signals entered from the microphones from one preset frequency width (sub-band) to another. The voice detection device also includes a band-based noise estimation unit that estimates the sub-band based noise power, and a sub-band based SNR calculation unit. The sub-band based SNR calculation unit calculates a sub-band SNR from one sub-band to another to output the largest one of the sub-band SNRs as an SNR for a microphone of interest. The voice detection device further includes a voice/non-voice decision unit that determines the voice/non-voice using the SNR for the microphone of interest.

Abstract: Acoustic models and language models are learned according to a speaking length which indicates a length of a speaking section in speech data, and speech recognition process is implemented by using the learned acoustic models and language models. A speech recognition apparatus includes means (103) for detecting a speaking section in speech data (101) and for generating a section information which indicates the detected speaking section, means (104) for recognizing a data part corresponding to a section information in the speech data as well as text data (102) written from the speech data and for classifying the data part based on a speaking length thereof, and means (106) for learning acoustic models and language models (107) by using the classified data part (105).

Abstract: A speech recognition apparatus (110) selects an optimum recognition result from recognition results output from a set of speech recognizers (s1-sM) based on a majority decision. This decision is implemented with taking into account weight values, as to the set of the speech recognizers, learned by a learning apparatus (100). The learning apparatus includes a unit (103) selecting speech recognizers corresponding to characteristics of speech for learning (101), a unit (104) finding recognition results of the speech for learning by using the selected speech recognizers, a unit (105) unifying the recognition results and generating a word string network, and a unit (106) finding weight values concerning a set of the speech recognizers by implementing learning processing.

Abstract: A warping factor estimation system comprises label information generation unit that outputs voice/non-voice label information, warp model storage unit in which a probability model representing voice and non-voice occurrence probabilities is stored, and warp estimation unit that calculates a warping factor in the frequency axis direction using the probability model representing voice and non-voice occurrence probabilities, voice and non-voice labels, and a cepstrum.

Abstract: To enable selection of a speaker, the acoustic feature value of which is similar to that of an utterance speaker, with accuracy and stability, while adapting to changes even when the acoustic feature value of the speaker changes every moment. A speaker score calculating means (22) calculates a long-time speaker score (log likelihood of each of a plurality of speaker models stored in a speaker model storage section (31) with respect to the acoustic feature value) based on an arbitrary number of utterances, for example, and calculates a short-time speaker score based on a short-time utterance, for example. A long-time speaker selecting means 23 selects speakers corresponding to a predetermined number of speaker models having a high long-time speaker score.

Abstract: A weighting factor learning system includes an audio recognition section that recognizes learning audio data and outputting the recognition result; a weighting factor updating section that updates a weighting factor applied to a score obtained from an acoustic model and a language model so that the difference between a correct-answer score calculated with the use of a correct-answer text of the learning audio data and a score of the recognition result becomes large; a convergence determination section that determines, with the use of the score after updating, whether to return to the weighting factor updating section to update the weighting factor again; and a weighting factor convergence determination section that determines, with the use of the score after updating, whether to return to the audio recognition section to perform the process again and update the weighting factor using the weighting factor updating section.

Abstract: A language model learning system for learning a language model on an identifiable basis relating to a word error rate used in speech recognition. The language model learning system (10) includes a recognizing device (101) for recognizing an input speech by using a sound model and a language model and outputting the recognized word sequence as the recognition result, a reliability degree computing device (103) for computing the degree of reliability of the word sequence, and a language model parameter updating device (104) for updating the parameters of the language model by using the degree of reliability. The language model parameter updating device updates the parameters of the language model to heighten the degree of reliability of the word sequence the computed degree of reliability of which is low when the recognizing device recognizes by using the updated language model and the reliability degree computing device computes the degree of reliability.

Abstract: A voice recognition system comprises an analyzer for converting an input voice signal to an input pattern including cepstrum, a reference pattern for storing reference patterns, an elongation/contraction estimating unit for outputting an elongation/contraction parameter in frequency axis direction by using the input pattern and the reference patterns, and a recognizing unit for calculating the distances between the converted input pattern from the converter and the reference patterns and outputting the reference pattern corresponding to the shortest distance as result of recognition. The elongation/contraction unit estimates an elongation/contraction parameter by using cepstrum included in the input pattern. The elongation/contraction unit does not have various values in advance for determining the elongation/contraction parameter, nor is it necessary for the elongation/contraction unit have to execute distance calculation for various values.

Abstract: A voice speed converter comprising a speech classifying unit for classifying an input speech signal into an unvoiced part and another part, a pitch frequency extracting unit for extracting a pitch frequency from the input speech signal and supplying it, a quasi-pitch frequency supplying unit for supplying a quasi-pitch frequency of fixed length, a voice speed converter for performing voice speed conversion processing on the input speech signal by the use of the pitch frequency or the quasi-pitch frequency, and a switch for controlling switching operations according to the classification result by the speech classifying unit, so as to send the quasi-pitch frequency to the voice speed converter when the input speech signal belongs to the unvoiced part, or so as to send the pitch frequency to the voice speed converter when the input speech signal belongs to another part.

Abstract: In a speech reproducing system, a speech coder receives an input speech signal to output a speech coded information including a pitch information of the input speech signal and a mode information indicative of a short-time characteristics of the input speech signal, and a speech decoder receives and decodes the speech coded information to generate a decoded speech signal. A speech-rate converter receives the pitch information and the mode information included in the speech coded information and the decoded speech signal, to convert the speech-rate of the decoded speech signal by using the pitch information and the mode information, thereby to generate an output speech signal.