Nippon Telegraph and Telephone Corporation

Shigeki Karita

Tomohiro Nakatani

Marc Delcroix

Atsunori Ogawa

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 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

A learning device () includes a feature extracting unit () that extracts features of speech from speech data for training, a probability calculating unit () 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 () 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 () that updates a parameter of the model in accordance with the parameter obtained by the error calculating unit ().

Growing community of inventors

Musashino, Japan

Shigeki Karita