Abstract: A speech recognition device and method configured to include a computer, for recognizing speech, including: a storage location for storing a feature quantity acquired from a speech signal for each frame; storage portions for storing acoustic model data and language model data; a echo speech component for generating echo speech model data from a speech signal acquired prior to a speech signal to be processed at the current time point and using the echo speech model data to generate adapted acoustic model data; and a processing component for utilizing the feature quantity, the adapted acoustic model data, and the language model data to provide a speech recognition result of the speech signal.

Abstract: Speech enhancement techniques for extemporaneous noise without a noise interval and unknown extemporaneous noise are provided with a method of signal enhancement including subtracting a given reference signal from an input signal containing a target signal and a noise signal by spectral subtraction; applying an adaptive filter to the reference signal; and controlling a filter coefficient of the adaptive filter in order to reduce components of the noise signal in the input signal. In signal enhancement, a database of a signal model concerning the target signal expressing a given feature by a given statistical model is provided, and the filter coefficient is controlled based on the likelihood of the signal model with respect to an output signal from the spectral subtraction means.

Abstract: Provided is a method for canceling background noise of a sound source other than a target direction sound source in order to realize highly accurate speech recognition, and a system using the same. In terms of directional characteristics of a microphone array, due to a capability of approximating a power distribution of each angle of each of possible various sound source directions by use of a sum of coefficient multiples of a base form angle power distribution of a target sound source measured beforehand by base form angle by using a base form sound, and power distribution of a non-directional background sound by base form, only a component of the target sound source direction is extracted at a noise suppression part. In addition, when the target sound source direction is unknown, at a sound source localization part, a distribution for minimizing the approximate residual is selected from base form angle power distributions of various sound source directions to assume a target sound source direction.

Abstract: Speech recognition is performed by matching between a characteristic quantity of an inputted speech and a composite HMM obtained by synthesizing a speech HMM (hidden Markov model) and a noise HMM for each speech frame of the inputted speech by use of the composite HMM.

Abstract: A speech recognition device and method configured to include a computer, for recognizing speech, including: a storage location for storing a feature quantity acquired from a speech signal for each frame; storage portions for storing acoustic model data and language model data; a echo speech component for generating echo speech model data from a speech signal acquired prior to a speech signal to be processed at the current time point and using the echo speech model data to generate adapted acoustic model data; and a processing component for utilizing the feature quantity, the adapted acoustic model data, and the language model data to provide a speech recognition result of the speech signal.

Abstract: To provide a method of specifying each of speakers of individual voices, based on recorded voices made by a plurality of speakers, with a simple system configuration, and to provide a system using the method. The system includes: microphones individually provided for each of the speakers; a voice processing unit which gives a unique characteristic to each pair of two-channel voice signals recorded with each of the microphones 10, by executing different kinds of voice processing on the respective pairs of voice signals, and which mixes the voice signals for each channel; and an analysis unit which performs an analysis according to the unique characteristics, given to the voice signals concerning the respective microphones through the processing by the voice processing unit, and which specifies the speaker for each speech segment of the voice signals.

Abstract: Provides speech enhancement techniques for extemporaneous noise without a noise interval and unknown extemporaneous noise. Signal enhancement includes: subtracting a given reference signal from an input signal containing a target signal and a noise signal by spectral subtraction; applying an adaptive filter to the reference signal; and controlling a filter coefficient of the adaptive filter in order to reduce components of the noise signal in the input signal. In signal enhancement, a database of a signal model concerning the target signal expressing a given feature by a given statistical model is provided, and the filter coefficient is controlled based on the likelihood of the signal model with respect to an output signal from the spectral subtraction means.

Abstract: Provided is a method for canceling background noise of a sound source other than a target direction sound source in order to realize highly accurate speech recognition, and a system using the same. In terms of directional characteristics of a microphone array, due to a capability of approximating a power distribution of each angle of each of possible various sound source directions by use of a sum of coefficient multiples of a base form angle power distribution of a target sound source measured beforehand by base form angle by using a base form sound, and power distribution of a non-directional background sound by base form, only a component of the target sound source direction is extracted at a noise suppression part. In addition, when the target sound source direction is unknown, at a sound source localization part, a distribution for minimizing the approximate residual is selected from base form angle power distributions of various sound source directions to assume a target sound source direction.

Abstract: Provides speech enhancement techniques which are effective even for extemporaneous noise without a noise interval and unknown extemporaneous noise. An example of a signal enhancement device includes: spectral subtraction means for subtracting a given reference signal from an input signal containing a target signal and a noise signal by spectral subtraction; an adaptive filter applied to the reference signal; and coefficient control means for controlling a filter coefficient of the adaptive filter in order to reduce components of the noise signal in the input signal. In the signal enhancement device, a database of a signal model concerning the target signal expressing a given feature by means of a given statistical model is provided, and the filter coefficient is controlled based on the likelihood of the signal model with respect to an output signal from the spectral subtraction means.

Abstract: Speech recognition is performed by matching between a characteristic quantity of an inputted speech and a composite HMM obtained by synthesizing a speech HMM (hidden Markov model) and a noise HMM for each speech frame of the inputted speech by use of the composite HMM.

Abstract: Provides speech enhancement techniques which are effective even for extemporaneous noise without a noise interval and unknown extemporaneous noise. An example of a signal enhancement device includes: spectral subtraction means for subtracting a given reference signal from an input signal containing a target signal and a noise signal by spectral subtraction; an adaptive filter applied to the reference signal; and coefficient control means for controlling a filter coefficient of the adaptive filter in order to reduce components of the noise signal in the input signal. In the signal enhancement device, a database of a signal model concerning the target signal expressing a given feature by means of a given statistical model is provided, and the filter coefficient is controlled based on the likelihood of the signal model with respect to an output signal from the spectral subtraction means.

Abstract: To provide a method of specifying each of speakers of individual voices, based on recorded voices made by a plurality of speakers, with a simple system configuration, and to provide a system using the method. The system includes: microphones individually provided for each of the speakers; a voice processing unit which gives a unique characteristic to each pair of two-channel voice signals recorded with each of the microphones 10, by executing different kinds of voice processing on the respective pairs of voice signals, and which mixes the voice signals for each channel; and an analysis unit which performs an analysis according to the unique characteristics, given to the voice signals concerning the respective microphones through the processing by the voice processing unit, and which specifies the speaker for each speech segment of the voice signals.

Abstract: A speech recognition device and method configured to include a computer, for recognizing speech, including: a storage location for storing a feature quantity acquired from a speech signal for each frame; storage portions for storing acoustic model data and language model data; a echo speech component for generating echo speech model data from a speech signal acquired prior to a speech signal to be processed at the current time point and using the echo speech model data to generate adapted acoustic model data; and a processing component for utilizing the feature quantity, the adapted acoustic model data, and the language model data to provide a speech recognition result of the speech signal.

Abstract: Speech recognition is performed by matching between a characteristic quantity of an inputted speech and a composite HMM obtained by synthesizing a speech HMM (hidden Markov model) and a noise HMM for each speech frame of the inputted speech by use of the composite HMM.

Abstract: Provided is a method for canceling background noise of a sound source other than a target direction sound source in order to realize highly accurate voice recognition, and a system using the same. In terms of directional characteristics of a microphone array, due to a capability of approximating a power distribution of each angle of each of possible various sound source directions by use of a sum of coefficient multiples of a base form angle power distribution of a target sound source measured beforehand by base form angle by using a base form sound, and power distribution of a non-directional background sound by base form, only a component of the target sound source direction is extracted at a noise suppression part. In addition, when the target sound source direction is unknown, at a sound source localization part, a distribution for minimizing the approximate residual is selected from base form angle power distributions of various sound source directions to assume a target sound source direction.