Abstract: 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.

Abstract: 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 b

Abstract: A data processing device includes processing circuitry configured to extract a second word corresponding to a first word included in first text from among a plurality of words belonging to a predetermined domain, repeat processing in the extraction for all words included in the first text to generate a confusion network that expresses a plurality of sentence possibilities with one network configuration and is an expression format of a word sequence, and search for a grammatically correct word string in the confusion network using a language model that evaluates grammatical correctness of the word string, and select a word string to be output.

Abstract: 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.

Abstract: 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.

Abstract: The extraction unit 132 extracts a second word corresponding to a first word included in a first text from among a plurality of words belonging to a predetermined domain. The determination unit 133 determines whether a predetermined condition for the word class of the first word is satisfied or not. When it is determined by the determination unit 133 that the condition is satisfied, the generation unit 134 generates a second text in which the first word of the first text is exchanged with the second word.

Abstract: A learning device (10) includes a feature extracting unit (11) that extracts features of speech from speech data for training, a probability calculating unit (12) that, on the basis of the features of speech, performs prefix searching using a speech recognition model of which a neural network is representative, and calculates a posterior probability of a recognition character string to obtain a plurality of hypothetical character strings, an error calculating unit (13) that calculates an error by word error rates of the plurality of hypothetical character strings and a correct character string for training, and obtains a parameter for the entire model that minimizes an expected value of summation of loss in the word error rates, and an updating unit (14) that updates a parameter of the model in accordance with the parameter obtained by the error calculating unit (13).

Abstract: An estimation apparatus clusters a group of voice signals including a voice signal having a speaker attribute to be estimated into a plurality of clusters. Subsequently, the estimation apparatus identifies, from the plurality of clusters, a duster to which the voice signal to be estimated belongs. Next, the estimation apparatus uses a speaker attribute estimation model to estimate speaker attributes of respective voice signals in the identified cluster. After that, the estimation apparatus estimates an attribute of the entire cluster, by using an estimation result of the speaker attributes of the voice signals in the identified cluster, and outputs an estimation result of the speaker attribute of the entire cluster, as an estimation result of the speaker attribute of the voice signal to be estimated.

Abstract: An audio signal processing apparatus (10) includes a first auxiliary feature conversion unit (12) and a second auxiliary feature conversion unit (13) 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 (11) 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.

Abstract: 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.

Abstract: A speech recognition unit (12) 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 (14) 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 (16) 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

Abstract: 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.

Abstract: To begin with, an acoustic model training apparatus extracts speech features representing speech characteristics, and calculates an acoustic-condition feature representing a feature of an acoustic condition of the speech data using an acoustic-condition calculation model that is represented as a neural network, based on an acoustic-condition calculation model parameter characterizing the acoustic-condition calculation model. The acoustic model training apparatus then generates an adjusted parameter that is an acoustic model parameter adjusted based on the acoustic-condition feature, the acoustic model parameter characterizing an acoustic model represented as a neural network to which an output layer of the acoustic-condition calculation model is coupled. The acoustic model training apparatus then updates the acoustic model parameter based on the adjusted parameter and the speech features, and updates the acoustic-condition calculation model parameters based on the adjusted parameter and the speech features.

Abstract: A learning device (10) includes a feature extracting unit (11) that extracts features of speech from speech data for training, a probability calculating unit (12) that, on the basis of the features of speech, performs prefix searching using a speech recognition model of which a neural network is representative, and calculates a posterior probability of a recognition character string to obtain a plurality of hypothetical character strings, an error calculating unit (13) that calculates an error by word error rates of the plurality of hypothetical character strings and a correct character string for training, and obtains a parameter for the entire model that minimizes an expected value of summation of loss in the word error rates, and an updating unit (14) that updates a parameter of the model in accordance with the parameter obtained by the error calculating unit (13).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A cluster weight calculator calculates weights corresponding to respective clusters in a mask calculation NN with at least one of the layers divided into the clusters, based on the signals of speech of a target speaker using a cluster weight calculation NN. A mask calculator calculates a mask for extracting features of speech of the target speaker from features in observed speech signals of one or more speakers based on the features in the observation signals of the speech of the one or more speakers using the mask calculator NN weighted by the weights calculated by the cluster weight calculator.