Pioneer Electronic Corporation

Pioneer Corporation

Techexperts Incorporation

Ikuo Fujita

Shunsuke Ishimitsu

Soichi Toyama

Mitsuya Komamura

An operator recognition device is provided that eliminates the registration of data such as HMM data having a characteristic amount for which error in recognition occurs easily when recognizing an operator, and thus reduces the possibility of errors in recognition, and has stable recognition performance. When registering HMM data that is used when performing recognition processing, a speaker recognition deviceeliminates the registration of HMM data of a password having a characteristic amount of the spoken voice component that is similar to a characteristic amount that is indicated by HMM data that is already registered, and does not allow the registration of HMM data for which it is estimated that error in recognition will occur easily during the recognition process.

Operator recognition device, operator recognition method and operator recognition program

A method of modifying feature parameters for a speech recognition is provided. This method is provided with: a process of extracting the feature parameter from an input speech in a real environment; a process of reading a first speech transfer characteristic corresponding to an environment in which a reference pattern for the speech recognition is generated, from a first memory device; a process of reading a second speech transfer characteristic corresponding to the real environment from a second memory device; a process of modifying the extracted feature parameter according to the first speech transfer characteristic and the second speech transfer characteristic to convert the extracted feature parameter corresponding to the real environment into a modified feature parameter corresponding to the environment in which the reference pattern is generated.

Method of modifying feature parameter for speech recognition, method of speech recognition and speech recognition apparatus

A speech model preparing method capable of easily preparing a new Hidden Markov Model (HMM) of an input speech with a very few number of utterances like one or two times, and a speech recognition apparatus using this method. A speech recognition apparatus uses, as a speech model, a continuous distribution type HMM defined by three parameters of a state transition probability, an average vector and a variance. The apparatus computes an average vector of an input speech to be learned, selects an HMM approximate to the input to-be-learned speech as an initial model from a registration dictionary, replaces at least an average vector of the selected HMM with the computed average vector of the to-be-learned speech and adds an obtained HMM as an HMM for the input to-be-learned speech in the dictionary.

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Kawagoe, Japan

Ikuo Fujita