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 frame processor 81 calculates, from a first sequence of feature vectors, a second sequence of frame-level feature vectors. A posterior estimator 82 calculates posterior probabilities for each vector included in the second sequence to a cluster. A statistics calculator 83 calculates a sufficient statistic used for extracting an i-vector by using the second sequence, the posterior probabilities, a mean vector of each cluster calculated at the time of learning of the frame processor 81 and the posterior estimator 82, and a global covariance matrix calculated based on the mean vector.

Abstract: Provided is to prevent a false determination due to an attachment condition of an apparatus that transmits and receives an acoustic signal, and perform accurate personal authentication. A personal authentication device includes: a personal authentication means that authenticates an individual by using first information at least including an acoustic characteristic calculated from an acoustic signal propagating through the head of the user, which is detected by an apparatus being attached on a head of a user for transmitting and receiving the acoustic signal, and a feature amount extracted from the acoustic characteristic; an attachment trouble rule storage means that stores an attachment trouble rule for detecting an attachment trouble with the apparatus; and an attachment trouble detection means that detects a trouble with an attachment state of the apparatus when the first information satisfies the attachment trouble rule.

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: 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 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: Provided is to prevent a false determination due to an attachment condition of an apparatus that transmits and receives an acoustic signal, and perform accurate personal authentication. A personal authentication device includes: a personal authentication means that authenticates an individual by using first information at least including an acoustic characteristic calculated from an acoustic signal propagating through the head of the user, which is detected by an apparatus being attached on a head of a user for transmitting and receiving the acoustic signal, and a feature amount extracted from the acoustic characteristic; an attachment trouble rule storage means that stores an attachment trouble rule for detecting an attachment trouble with the apparatus; and an attachment trouble detection means that detects a trouble with an attachment state of the apparatus when the first information satisfies the attachment trouble rule.

Abstract: The information processing device is provided in a feature extraction block in a neural network. The information processing device acquires a local feature quantity group constituting one unit of information, and computes a weight corresponding to a degree of importance of each local feature quantity. Next, the information processing device computes a weighted statistic for a whole of the local feature quantity group using the computed weights, and deforms and outputs the local feature quantity group using the computed weighted statistic.

Abstract: Provided is a personal authentication device capable of simply securing security with little psychological and physical burden of a user to be authenticated. Personal authentication device includes: transmission unit that transmits a first acoustic signal to a user's head; observation unit that observes a second acoustic signal after the first acoustic signal propagation; calculation unit that calculates acoustic characteristics from the first and the second acoustic signal; extraction unit that extracts a feature amount related to a user from the acoustic characteristics; storage control unit that registers the feature amount in the storage unit; identification unit that identifies the user by collating the first feature amount with a second feature amount; and storage unit stores the first feature amount, wherein while identification unit identifies the user as being identical, transmission unit transmits the first acoustic signal every predetermined interval.

Abstract: A spoofing detection apparatus 100 includes a multi-channel spectrogram creation unit 10 and an evaluation unit 40. The multi-channel spectrogram creation unit 10 extracts different type of spectrograms from speech data and integrates the different type of spectrograms to create a multi-channel spectrogram. The evaluation unit 40 evaluates the created multi-channel spectrogram by applying the created multi-channel spectrogram to a classifier constructed using labeled multi-channel spectrograms as training data and classifies it to either genuine or spoof.

Abstract: A spoofing detection apparatus 100 includes a multi-channel spectrogram creation unit 10 and an evaluation unit 40. The multi-channel spectrogram creation unit 10 extracts different type of spectrograms from speech data and integrates the different type of spectrograms to create a multi-channel spectrogram. The evaluation unit 40 evaluates the created multi-channel spectrogram by applying the created multi-channel spectrogram to a classifier constructed using labeled multi-channel spectrograms as training data and classifies it to either genuine or spoof.

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: An input unit 81 inputs an observation at current time step. A frame alignment unit 82 computes a frame alignment at a current time step by using the input observation. An i-vector computation unit 83 computes an i-vector and a precision matrix by using the computed frame alignment, the input observation, and a product obtained when computing the i-vector at the previous time step. An output unit 84 outputs the computed i-vector and precision matrix.

Abstract: A pattern recognition apparatus for discriminative training includes: a similarity calculator that calculates similarities among training data; a statistics calculator that calculates statistics from the similarities in accordance with current labels for the training data; and a discriminative probabilistic linear discriminant analysis (PLDA) trainer that receives the training data, the statistics of the training data, the current labels and PLDA parameters, and updates the PLDA parameters and the labels of the training data.

Abstract: A neural network input unit 81 inputs a neural network in which a first network having a layer for inputting an anchor signal belonging to a predetermined class and a mixed signal including a target signal belonging to the class and a layer for outputting, as an estimation result, a reconstruction mask indicating a time-frequency domain in which the target signal is present in the mixed signal, and a second network having a layer for inputting the target signal extracted by applying the mixed signal to the reconstruction mask and a layer for outputting a result obtained by classifying the input target signal into a predetermined class are combined. A reconstruction mask estimation unit 82 applies the anchor signal and mixed signal to the first network to estimate the reconstruction mask of the class to which the anchor signal belongs.

Abstract: A speech processing device includes: first segment means for dividing first speech into a plurality of first speech segments; second segment means for dividing second speech into a plurality of second speech segments; primary speaker recognition means for calculating scores indicating similarities between the plurality of first and second speech segments; threshold value calculation means for calculating a threshold value based on scores indicating similarities between the plurality of first speech segments; speaker clustering means for classifying each of the plurality of second speech segments into one or more clusters having a similarity higher than the similarity indicated by the threshold value; and secondary speaker recognition means for calculating a similarity between each of the one or more clusters and the first speech and determining based on a result of the calculation whether speech corresponding to the first speech is contained in any of the one or more clusters.

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: 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: Provided is a first acoustic information acquisition unit configured to acquire a first acoustic information obtained by receiving a sound wave emitted from a first sound source by a wearable device worn by a user, a second acoustic information acquisition unit configured to acquire a second acoustic information obtained by receiving a sound wave emitted from a second sound source that is different from the first sound source by the wearable device, and a third acoustic information acquisition unit configured to acquire a third acoustic information used for biometric matching of the user based on the first acoustic information and the second acoustic information.

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.