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 feature calculation means calculates N features for first data and N features for second data by using N feature functions for obtaining a feature for data on the basis of the data. A similarity degree calculation means calculates a similarity degree between the first data and the second data on the basis of the N features for the first data and the N features for the second data. Values of N features obtained when the same data is substituted into the N feature functions are different from each other.

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: 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: 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: 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 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 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: Embodiments of systems and methods for speaker verification are provided. In various embodiments, a method includes receiving an utterance from a speaker and determining a text-independent speaker verification score and a text-dependent speaker verification score in response to the utterance. Various embodiments include a system for speaker verification, the system comprising an audio receiving device for receiving an utterance from a speaker and converting the utterance to an utterance signal, and a processor coupled to the audio receiving device for determining speaker verification in response to the utterance signal, wherein the processor determines speaker verification in response to a UBM-independent speaker-normalized score.

Abstract: Embodiments of systems and methods for speaker verification are provided. In various embodiments, a method includes receiving an utterance from a speaker and determining a text-independent speaker verification score and a text-dependent speaker verification score in response to the utterance. Various embodiments include a system for speaker verification, the system comprising an audio receiving device for receiving an utterance from a speaker and converting the utterance to an utterance signal, and a processor coupled to the audio receiving device for determining speaker verification in response to the utterance signal, wherein the processor determines speaker verification in response to a UBM-independent speaker-normalized score.