Abstract: This speech processing device is provided with: a contribution degree estimation means which calculates a contribution degree representing a quality of a segment of the speech signal; and a speaker feature calculation means which calculates a feature from the speech signal, for recognizing attribute information of the speech signal, using the contribution degree as a weight of the segment of the speech signal.

Abstract: An audio processing apparatus 100 is apparatus for generating a training data in speaker recognition. The audio processing apparatus 100 includes a data acquisition unit configured to acquire an audio signal that is a source of the training data as sample data, a data generation unit configured to executes signal processing on the acquired sample data, and to generates a new audio signal as the training data whose similarity with the sample data is within the set range.

Abstract: A covariance matrix computation unit 81 computes a pseudo-in-domain covariance matrix from one or both of a within class covariance matrix and a between class covariance matrix of an out-of-domain Probabilistic Linear Discriminant Analysis (PLDA) model. A simultaneous diagonalization unit 82 computes a generalized eigenvalue and an eigenvector for a pseudo-in-domain covariance matrix and the class covariance matrix of the out-of-domain PLDA model on the basis of simultaneous diagonalization. An adaptation unit 83 computes one or both of a within class covariance matrix and a between class covariance matrix of an in-domain PLDA model using the generalized eigenvalues and eigenvectors. The covariance matrix computation unit 81 computes the pseudo-in-domain covariance matrix based on the out-of-domain PLDA model and a covariance matrix of in-domain data.

Abstract: A voice processing device, a voice processing method, and a program recording medium that enhance accuracy of speaker recognition are provided. A voice processing device 100 includes a voice statistics calculation unit 120 that calculates voice statistics that indicates an appearance of each type of sound included in a voice signal that indicates a voice, and a second feature calculation unit 140 that calculates a second feature to recognize specific attribute information based on a temporal change of the voice statistics.

Abstract: An authentication device is provided with: a plurality of attribute-dependent score calculation units each calculating an attribute-dependent score dependent on a prescribed attribute for input data; an attribute-independent score calculation unit for calculating an attribute-independent score independent of the attribute for the input data; an attribute estimation unit for performing attribute estimation for the input data; and a score integration unit for determining a score weight of each of a plurality of attribute-dependent scores and of the attribute-independent score using the result of the attribute estimation and calculating an output score using the attribute-dependent scores, the attribute-independent score, and the determined score weights.

Abstract: The information processing apparatus (2000) computes a first score representing a degree of similarity between the input sound data (10) and the registrant sound data (22) of the registrant (20). The information processing apparatus (2000) obtains a plurality of pieces of segmented sound data (12) by segmenting the input sound data (10) in the time direction. The information processing apparatus (2000) computes, for each piece of segmented sound data piece (12), a second score representing the degree of similarity between the segmented sound data (12) and the registrant sound data (22). The information processing apparatus 2000 makes first determination to determine whether a number of speakers of sound included in the input sound data (10) is one or multiple, using at least the second score.

Abstract: To provide an attribute identifying device, an attribute identifying method, and a program storage medium in which the accuracy of attribute identification of a person is further enhanced. An attribute identifying device 100 includes a first attribute identifying unit 130 that identifies, based on a biological signal, first attribute information, which is a range of specific attribute values, from the biological signal, and a second attribute identifying unit 140 that identifies second attribute information, which is specific attribute information, from the biological signal and the first attribute information.

Abstract: A speech feature extraction apparatus 100 includes a voice activity detection unit 103 that drops non-voice frames from frames corresponding to an input speech utterance, and calculates a posterior of being voiced for each frame, a voice activity detection process unit 106 calculates a function value as weights in pooling frames to produce an utterance-level feature, from a given a voice activity detection posterior, and an utterance-level feature extraction unit 112 that extracts an utterance-level feature, from the frame on a basis of multiple frame-level features, using the function values.

Abstract: An authentication device is provided with: a plurality of attribute-dependent score calculation units each calculating an attribute-dependent score dependent on a prescribed attribute for input data; an attribute-independent score calculation unit for calculating an attribute-independent score independent of the attribute for the input data; an attribute estimation unit for performing attribute estimation for the input data; and a score integration unit for determining a score weight of each of a plurality of attribute-dependent scores and of the attribute-independent score using the result of the attribute estimation and calculating an output score using the attribute-dependent scores, the attribute-independent score, and the determined score weights.

Abstract: A biometric authentication device is provided with: a replay unit for reproducing a sound; an ear authentication unit for acquiring a reverberation sound of the sound in an ear of a user to be authenticated, extracting an ear acoustic feature from the reverberation sound, and calculating an ear authentication score by comparing the extracted ear acoustic feature with an ear acoustic feature stored in advance; a voice authentication unit for extracting a talker feature from a voice of the user that has been input, and calculating a voice authentication score by comparing the extracted talker feature with a talker feature stored in advance; and an authentication integration unit for outputting an authentication integration result calculated based on the ear authentication score and the voice authentication score. After the sound is output into the ear, a recording unit inputs the voice of the user.

Abstract: The accuracy of ear authentication is ensured over a range from an audible range to a non-audible range. An earphone includes a sound emitting unit, a sound collecting unit, a housing containing the sound emitting unit and the sound collecting unit, a sound hole formed inside the housing, the sound hole being configured to propagate an emitted sound to a predetermined direction and propagate a sound coming from the predetermined direction to the sound collecting unit, and an ear pad covering at least a part of the sound hole, in which an ear-pad side end face is configured so as not to project beyond a sound-hole side end face at least in the predetermined direction, the ear-pad side end face being an end face of the ear pad, the sound-hole side end face being an end face of the sound hole in the predetermined direction.

Abstract: This speech processing device is provided with: a contribution degree estimation means which calculates a contribution degree representing a quality of a segment of the speech signal; and a speaker feature calculation means which calculates a feature from the speech signal, for recognizing attribute information of the speech signal, using the contribution degree as a weight of the segment of the speech signal.

Abstract: An authentication device is provided with: a plurality of attribute-dependent score calculation units each calculating an attribute-dependent score dependent on a prescribed attribute for input data; an attribute-independent score calculation unit for calculating an attribute-independent score independent of the attribute for the input data; an attribute estimation unit for performing attribute estimation for the input data; and a score integration unit for determining a score weight of each of a plurality of attribute-dependent scores and of the attribute-independent score using the result of the attribute estimation and calculating an output score using the attribute-dependent scores, the attribute-independent score, and the determined score weights.

Abstract: Provided are a personal authentication device performing accurate authentication immediately follow insertion an earphone/microphone device to wear, and preventing spoofing after authentication.

Abstract: A feature vector having high class identification capability is generated. A signal processing system provided with: a first generation unit for generating a first feature vector on the basis of one of time-series voice data, meteorological data, sensor data, and text data, or on the basis of a feature quantity of one of these; a weight calculation unit for calculating a weight for the first feature vector; a statistical amount calculation unit for calculating a weighted average vector and a weighted high-order statistical vector of second or higher order using the first feature vector and the weight; and a second generation unit for generating a second feature vector using the weighted high-order statistical vector.

Abstract: The speech processing apparatus 100 includes an air microphone speech recognition unit 101 which recognizes speech from an air microphone 200 acquiring speech through air, a wearable microphone speech recognition unit 102 which recognizes speech from a wearable microphone 300, a sensing unit 103 which measures environmental conditions, a weight decision unit 104 which calculates the weights for recognition results of the air microphone speech recognition unit 101 and the wearable microphone speech recognition unit 102 on the basis of the environmental conditions, and a combination unit 105 which combines the recognition results outputted from the air microphone speech recognition unit 101 and the wearable microphone speech recognition unit 102, using the weights.

Abstract: A personal authentication device includes: acoustic signal transmission means 701 for transmitting a first acoustic signal to a part of a head of a user; acoustic signal observation means 702 for observing a second acoustic signal which is an acoustic signal after the first acoustic signal propagates through the part of the head; acoustic property calculation means 703 for calculating an acoustic property from the first acoustic signal and the second acoustic signal; and user identification means 704 for identifying the user, based on the acoustic property or a feature value extracted from the acoustic property and relating to the user.

Abstract: A speaker recognition system includes a non-transitory computer readable medium configured to store instructions. The speaker recognition system further includes a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for extracting acoustic features from each frame of a plurality of frames in input speech data. The processor is configured to execute the instructions for calculating a saliency value for each frame of the plurality of frames using a first neural network (NN) based on the extracted acoustic features, wherein the first NN is a trained NN using speaker posteriors. The processor is configured to execute the instructions for extracting a speaker feature using the saliency value for each frame of the plurality of frames.

Abstract: A speaker recognition system includes a non-transitory computer readable medium configured to store instructions. The speaker recognition system further includes a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for extracting acoustic features from each frame of a plurality of frames in input speech data. The processor is configured to execute the instructions for calculating a saliency value for each frame of the plurality of frames using a first neural network (NN) based on the extracted acoustic features, wherein the first NN is a trained NN using speaker posteriors. The processor is configured to execute the instructions for extracting a speaker feature using the saliency value for each frame of the plurality of frames.

Abstract: Provided is a pattern recognition apparatus to provide classification robustness to any kind of domain variability. The pattern recognition apparatus 500 based on Neural Network (NN) includes: NN training unit 501 that trains an NN model to generate NN parameters, based on at least one first feature vector and at least one domain vector indicating one of subsets in a specific domain, wherein, the first feature vector is extracted from each of the subsets, the domain vector indicates an identifier corresponding to the each of the subsets; and NN verification unit 502 that verifies a pair of second feature vectors in the specific domain to output whether the pair indicates same individual or not, based on a target domain vector and the NN parameters.