A speaker recognition system for assessing the identity of a speaker through a speech signal based on speech uttered by said speaker is provided. The system includes a framing module that subdivides the speech signal over time into a set of frames, and a filtering module that analyzes the frames of the set to discard frames affected by noise and frames which do not comprise a speech, based on a spectral analysis of the frames. A feature extraction module extracts audio features from frames which have not been discarded, and a classification module processes the audio features extracted from the frames which have not been discarded for assessing the identity of the speaker.

A speaker recognition system for assessing the identity of a speaker through a speech signal based on speech uttered by said speaker is provided. The system includes a framing module that subdivides the speech signal over time into a set of frames, and a filtering module that analyzes the frames of the set to discard frames affected by noise and frames which do not comprise a speech, based on a spectral analysis of the frames. A feature extraction module extracts audio features from frames which have not been discarded, and a classification module processes the audio features extracted from the frames which have not been discarded for assessing the identity of the speaker.

An automated speaker verification (ASV) system incorporates a first deep neural network to extract deep acoustic features, such as deep CQCC features, from a received voice sample. The deep acoustic features are processed by a second deep neural network that classifies the deep acoustic features according to a determined likelihood of including a spoofing condition. A binary classifier then classifies the voice sample as being genuine or spoofed.

An automated speaker verification (ASV) system incorporates a first deep neural network to extract deep acoustic features, such as deep CQCC features, from a received voice sample. The deep acoustic features are processed by a second deep neural network that classifies the deep acoustic features according to a determined likelihood of including a spoofing condition. A binary classifier then classifies the voice sample as being genuine or spoofed.

Provided is to prevent a false determination due to an attachment condition of an apparatus that transmits and receives an acoustic signal, and perform accurate personal authentication. A personal authentication device includes: a personal authentication means that authenticates an individual by using first information at least including an acoustic characteristic calculated from an acoustic signal propagating through the head of the user, which is detected by an apparatus being attached on a head of a user for transmitting and receiving the acoustic signal, and a feature amount extracted from the acoustic characteristic; an attachment trouble rule storage means that stores an attachment trouble rule for detecting an attachment trouble with the apparatus; and an attachment trouble detection means that detects a trouble with an attachment state of the apparatus when the first information satisfies the attachment trouble rule.

Provided is to prevent a false determination due to an attachment condition of an apparatus that transmits and receives an acoustic signal, and perform accurate personal authentication. A personal authentication device includes: a personal authentication means that authenticates an individual by using first information at least including an acoustic characteristic calculated from an acoustic signal propagating through the head of the user, which is detected by an apparatus being attached on a head of a user for transmitting and receiving the acoustic signal, and a feature amount extracted from the acoustic characteristic; an attachment trouble rule storage means that stores an attachment trouble rule for detecting an attachment trouble with the apparatus; and an attachment trouble detection means that detects a trouble with an attachment state of the apparatus when the first information satisfies the attachment trouble rule.

An audio system receives an audio signal from a digital microphone, which has an analog-digital converter with a controllable sampling rate. In response to a determination that a predetermined trigger phrase is not detected in the decimated audio signal, the sampling rate of the analog-digital converter in the digital microphone is controlled such that the audio signal has a first sample rate. In response to a determination that the predetermined trigger phrase is detected in the decimated signal, the sampling rate of the analog-digital converter in the digital microphone is controlled such that the audio signal has a second sample rate higher than the first sample rate, and the audio signal is applied to a spoof detection circuit, to determine whether the received signal contains live speech or replayed speech.

An audio system receives an audio signal from a digital microphone, which has an analog-digital converter with a controllable sampling rate. In response to a determination that a predetermined trigger phrase is not detected in the decimated audio signal, the sampling rate of the analog-digital converter in the digital microphone is controlled such that the audio signal has a first sample rate. In response to a determination that the predetermined trigger phrase is detected in the decimated signal, the sampling rate of the analog-digital converter in the digital microphone is controlled such that the audio signal has a second sample rate higher than the first sample rate, and the audio signal is applied to a spoof detection circuit, to determine whether the received signal contains live speech or replayed speech.

Disclosed is an electronic apparatus which identifies utterer characteristics of an uttered voice input received; identifies one utterer group among a plurality of utterer groups based on the identified utterer characteristics; outputs a recognition result among a plurality of recognition results of the uttered voice input based on a voice recognition model corresponding to the identified utterer group among a plurality of voice recognition models provided corresponding to the plurality of utterer groups, the plurality of recognition results being different in recognition accuracy from one another; identifies recognition success or failure in the uttered voice input with respect to the output recognition result; and changes a recognition accuracy of the output recognition result in the voice recognition model corresponding to the recognition success, based on the identified recognition success in the uttered voice input.

Disclosed is an electronic apparatus which identifies utterer characteristics of an uttered voice input received; identifies one utterer group among a plurality of utterer groups based on the identified utterer characteristics; outputs a recognition result among a plurality of recognition results of the uttered voice input based on a voice recognition model corresponding to the identified utterer group among a plurality of voice recognition models provided corresponding to the plurality of utterer groups, the plurality of recognition results being different in recognition accuracy from one another; identifies recognition success or failure in the uttered voice input with respect to the output recognition result; and changes a recognition accuracy of the output recognition result in the voice recognition model corresponding to the recognition success, based on the identified recognition success in the uttered voice input.