Panasonic Intellectual Property Corporation of America

Kousuke Itakura

Ko Mizuno

Misaki Doi

Takahiro Kamai

A speaker identification device acquires identification target voice data; acquires registered voice data; selects a first speaker identification model machine-learned using male voice data to identify a male speaker in a case where one of a sex of a speaker of the identification target voice data and a sex of a speaker of the registered voice data is male, and selects a second speaker identification model machine-learned using female voice data to identify a female speaker in a case where one of a sex of the speaker of the identification target voice data and a sex of the speaker of the registered voice data is female; and inputs a feature amount of the identification target voice data and a feature amount of the registered voice data to one of the selected first speaker identification model and second speaker identification model to identify the speaker of the identification target voice data.

Speaker identification method, speaker identification device, non-transitory computer readable recording medium storing speaker identification program, sex identification model generation method, and speaker identification model generation method

A training method of training a speaker identification model which receives voice data as an input and outputs speaker identification information for identifying a speaker of an utterance included in the voice data is provided. The training method includes: performing voice quality conversion of first voice data of a first speaker to generate second voice data of a second speaker; and performing training of the speaker identification model using, as training data, the first voice data and the second voice data.

Training method of a speaker identification model based on a first language and a second language

In a behavior identification method, surrounding sound is acquired, a feature value that is specified by a spectrum pattern included in spectrum information generated from sound made by a person performing a predetermined behavior is extracted from the sound acquired, the predetermined behavior is identified by the feature value, and information indicating the predetermined behavior identified is output.

Behavior identification method, behavior identification device, non-transitory computer-readable recording medium recording therein behavior identification program, machine learning method, machine learning device, and non-transitory computer-readable recording medium recording therein machine learning program

A speaker recognition device according to the present disclosure includes: an acoustic feature calculator that calculates, from utterance data indicating a voice of an obtained utterance, acoustic feature of the voice of the utterance; a statistic calculator that calculates an utterance data statistic from the calculated acoustic feature; a speaker feature extractor that extracts speaker feature of a speaker of the utterance data from the calculated utterance data statistic using a deep neural network (DNN); a similarity calculator that calculates a similarity between the extracted speaker feature and pre-stored speaker feature of at least one registered speaker; and a speaker recognizer that recognizes the speaker of the utterance data based on the calculated similarity.

Speaker recognition device, speaker recognition method, and recording medium

A speaker recognition device includes: a feature calculator that calculates two or more acoustic features of a voice of an utterance obtained; a similarity calculator that calculates two or more similarities, each being a similarity between one of one or more speaker-specific features of a target speaker for recognition and one of the two or more acoustic features; a combination unit that combines the two or more similarities to obtain a combined value; and a determiner that determines whether a speaker of the utterance is the target speaker based on the combined value. Here, (i) at least two of the two or more acoustic features have different properties, (ii) at least two of the two or more similarities have different properties, or (iii) at least two of the two or more acoustic features have different properties and at least two of the two or more similarities have different properties.

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Osaka, Japan

Kousuke Itakura