Nippon Telegraph and Telephone Corporation

Georgia Tech Research Corporation

Wakayama University

Tomohiro Nakatani

Keisuke Kinoshita

Marc Delcroix

Shoko Araki

Atsunori Ogawa

Nobutaka Ito

Takuya Yoshioka

Rintaro Ikeshita

Shigeki Karita

Takuya Higuchi

Masato Miyoshi

Hiroshi Sawada

Tsubasa Ochiai

Hiroshi Sato

Biing-Hwang Juang

Masaaki Nagata

Tsutomu Hirao

Katsuhiko Yamamoto

Yuki Kubo

Toshio Irino

Lukas Drude

Thilo Christoph Von Neumann

Toshie Matsui

Mehrez Souden

Michael Hentschel

Masakiyo Fujimoto

An audio signal processing apparatus () includes a first auxiliary feature conversion unit () and a second auxiliary feature conversion unit () that convert a plurality of signals relating to processing of an audio signal of a target speaker into a plurality of auxiliary features for the plurality of signals using a plurality of auxiliary neural networks corresponding to the plurality of signals, and an audio signal processing unit () that estimates information regarding an audio signal of the target speaker included in a mixed audio signal using a main neural network based on an input feature of the mixed audio signal and the plurality of auxiliary features, wherein the plurality of signals relating to processing of the audio signal of the target speaker are two or more pieces of information of different modalities.

Speech signal processing device, speech signal processing method, speech signal processing program, training device, training method, and training program

A sound source signal generation technology based on an optimization algorithm that enables high-speed processing of sound source extraction is provided. A sound source signal generation device includes an optimization unit that optimizes a separation matrix W(f)=[w(f), . . . , w(f), W(f)] using an observed signal x(f, t), the optimization unit includes an auxiliary function calculation unit that calculates an auxiliary function V(f) (i=1, . . . , K) according to a predetermined equation, a first separation filter calculation unit that calculates a separation filters w(f) (i=1, . . . , K) using auxiliary functions V(f) (i=1, . . . , K) and V(f), and a second separation filter calculation unit that calculates a separation filter W(f) according to a predetermined equation when a convergence condition is satisfied.

Target source signal generation apparatus, target source signal generation method, and program

Provided is an acoustic signal enhancement device, including a time-space covariance matrix estimation unit configured to estimate a time-space covariance matrix corresponding to a sound source, using a power of the sound source and an observation signal vector composed of an observation signal from a microphone. A reverberation suppression unit is configured to obtain a reverberation removal filter of the sound source using the time-space covariance matrix, and to generate a reverberation suppression signal vector corresponding to the observation signal for an emphasized sound of the sound source using the reverberation removal filter and the observation signal vector. A sound source separation unit is configured to obtain an emphatic sound of the sound source and the power of the sound source using the reverberation suppression signal vector.

Acoustic signal enhancement apparatus, method and program

Disclosed is a model adaptation technology of a language model with higher adaptability. An aspect of the present disclosure relates to an apparatus includes a first neural network unit that transforms an input symbol and outputs an intermediate state; and a second neural network unit that transforms input auxiliary information and the intermediate state and predicts a symbol following the input symbol, wherein the second neural network unit includes a plurality of hidden layers receiving, as input, the intermediate state and auxiliary information, and pieces of the auxiliary information input to each hidden layer are different from each other.

Apparatus, method, and program for utilizing language model

A mask estimation apparatus includes processing circuitry configured to estimate, for a target segment to be processed among a plurality of segments of a continuous time, a first mask which is an occupancy ratio of a target signal to an observation signal of the target segment, based on a first feature obtained from a plurality of the observation signals of the target segment recorded at a plurality of locations, and estimate a parameter for modeling a second feature and a second mask which is an occupancy ratio of the target signal to the observation signal based on an estimation result of the first mask in the target segment and the second feature obtained from the plurality of the observation signals of the target segment.

Mask estimation device, mask estimation method, and mask estimation program

Provided is a target sound extraction technique based on a steering vector generation method enabling instability in a calculation to be prevented when a neural network is trained by using an error back propagation method to reduce an estimation error of a beamformer. A target sound signal generation apparatus generates a target sound signal ycorresponding to a target sound included in an observed sound from an observed signal vector xcorresponding to the observed sound collected by using a plurality of microphones. The target sound signal generation apparatus includes a mask generation unit, a steering vector generation unit, a beamformer vector generation unit, and a target sound signal generation unit. The mask generation unit is configured as a neural network trained by using an error back propagation method. The steering vector generation unit generates a steering vector hby determining an eigenvector corresponding to a maximum eigenvalue of a predetermined matrix generated from the observed signal vector xand a mask γby using a power method.

Target sound signal generation apparatus, target sound signal generation method, and program

A learning device includes a memory, and processing circuitry coupled to the memory and configured to receive an input of a plurality of series for learning having known accuracy, and learn a model represented by a neural network, the model being capable of determining accuracy levels of two series when given feature amounts of the two series among the plurality of series.

Learning device, learning method, and learning program

A reranking device include a hypothesis input unit configured to receive input of N-best hypotheses associated with scores of a speech recognition accuracy; a hypothesis selection unit configured to select two hypotheses to be determined from among the input N-best hypotheses. Further, there is a determination unit configured to determine which accuracy of two hypotheses is higher by using: a plurality of first auxiliary model to M-th auxiliary model represented by such a neural network as to be capable of converting, when the selected two hypotheses are given, the two hypotheses into hidden state vectors, and determining which of the two hypotheses is higher based on the hidden state vectors of the two hypotheses; and a main model represented by such a neural network as to be capable of determining which of the two hypotheses is higher based on the hidden state vectors of the two hypotheses.

Determination device, training device, determination method, and determination program

A signal processing device according to an embodiment of the present invention includes: a conversion unit configured to convert an input mixed acoustic signal into a plurality of first internal states, a weighting unit configured to generate a second internal state which is a weighted sum of the plurality of first internal states based on auxiliary information regarding an acoustic signal of a target sound source when the auxiliary information is input, and generate the second internal state by selecting one of the plurality of first internal states when the auxiliary information is not input, and a mask estimation unit configured to estimate a mask based on the second internal state.

Signal processing apparatus, learning apparatus, signal processing method, learning method and program

A sound source separation filter information estimation device () estimates a covariance matrix having information on a correlation between sound source spectra and information on a correlation between channels as information on sound source separation filter information for separating an individual sound source signal from a mixed acoustic signal.

Estimation device, estimation method, and estimation program

The signal separation device includes: cross product calculation means receiving an input of an observed signal that is a mixture of a plurality of target signals, and calculating a cross product of the observed signal; model calculation means updating a parameter of a model for estimating the cross product with a predetermined algorithm using an inverse matrix of a matrix that represents an estimate of the cross product; inverse matrix calculation means calculating the inverse matrix of a matrix by a SIMD command when the parameter is updated; and separation means calculating the target signals using a matrix representing an estimate of the cross product, the updated parameter, and the observed signal.

Signal separation apparatus, signal separation method and program

To sufficiently suppress noise and reverberation, a convolutional beamformer for calculating, at each time point, a weighted sum of a current signal and a past signal sequence having a predetermined delay and a length of 0 or more such that it increases a probability expressing a speech-likeness of an estimation signals based on a predetermined probability model is acquired where the estimation signals are acquired by applying the convolutional beamformer to frequency-divided observation signals corresponding respectively to a plurality of frequency bands of observation signals acquired by picking up acoustic signals emitted from a sound source, whereupon target signals are acquired by applying the acquired convolutional beamformer to the frequency-divided observation signals.

Signal processing apparatus, signal processing method, and program

A speech recognition unit converts an input utterance sequence into a confusion network sequence constituted by a k-best of candidate words of speech recognition results; a lattice generating unit generates a lattice sequence having the candidate words as internal nodes and a combination of k words among the candidate words for an identical speech as an external node, in which edges are extended between internal nodes other than internal nodes included in an identical external node, from the confusion network sequence; an integer programming problem generating unit generates an integer programming problem for selecting a path that maximizes an objective function including at least a coverage score of an important word, of paths following the internal nodes with the edges extended, in the lattice sequence; and the summary generating unit generates a high-quality summary having less speech recognition errors and low redundancy using candidate words indicated by the internal nodes included in the path selected by solving the integer programming problem, under a constraint on the length of a summary to be generated.

Abstract generation device, method, program, and recording medium

A determination device includes a memory, and processing circuitry coupled to the memory and configured to accept input of a plurality of sequences provided as candidates for a solution to one given input, and determine, for two sequences of the plurality of sequences, a sequence that has a higher accuracy than the other sequence of the two sequences, using a model expressed as a neural network.

Determination device, determination method, and determination program

Features are extracted from an observed speech signal including at least speech of multiple speakers including a target speaker. A mask is calculated for extracting speech of the target speaker based on the features of the observed speech signal and a speech signal of the target speaker serving as adaptation data of the target speaker. The signal of the speech of the target speaker is calculated from the observed speech signal based on the mask. Speech of the target speaker can be extracted from observed speech that includes speech of multiple speakers.

Mask calculation device, cluster weight learning device, mask calculation neural network learning device, mask calculation method, cluster weight learning method, and mask calculation neural network learning method

Growing community of inventors

Kyoto, Japan

Tomohiro Nakatani