Abstract: Techniques for unobtrusively verifying the identity of a user of a computing device are provided. In one embodiment, the computing device can establish one or more verification models for verifying the user's identity, where at least a subset of the one or more verification models is based on enrollment data that is collected in an unobtrusive manner from the user. The computing device can then verify the user's identity using the one or more verification models.

Abstract: In one embodiment, a computing device can detect an utterance of a target phrase within an acoustic input signal. The computing device can further determine a first estimate of cumulative signal and noise energy for the detected utterance in the acoustic input signal with respect to a first time period spanning the duration of the detected utterance, and a second estimate of noise energy in the acoustic input signal with respect to a second time period preceding (or following) the first time period. The computing device can then calculate a signal-to-noise ratio (SNR) for the detected utterance based on the first and second estimates and can reject the detected utterance if the SNR is below an SNR threshold.

Abstract: In one embodiment, a computer system stores speech data for a plurality of speakers, where the speech data includes a plurality of feature vectors and, for each feature vector, an associated sub-phonetic class. The computer system then builds, based on the speech data, an artificial neural network (ANN) for modeling speech of a target speaker in the plurality of speakers, where the ANN is configured to discriminate between instances of sub-phonetic classes uttered by the target speaker and instances of sub-phonetic classes uttered by other speakers in the plurality of speakers.

Abstract: In one embodiment, a computing device can detect an utterance of a target phrase within an acoustic input signal. The computing device can further determine a first estimate of cumulative signal and noise energy for the detected utterance in the acoustic input signal with respect to a first time period spanning the duration of the detected utterance, and a second estimate of noise energy in the acoustic input signal with respect to a second time period preceding (or following) the first time period. The computing device can then calculate a signal-to-noise ratio (SNR) for the detected utterance based on the first and second estimates and can reject the detected utterance if the SNR is below an SNR threshold.

Abstract: Techniques for unobtrusively verifying the identity of a user of a computing device are provided. In one embodiment, the computing device can establish one or more verification models for verifying the user's identity, where at least a subset of the one or more verification models is based on enrollment data that is collected in an unobtrusive manner from the user. The computing device can then verify the user's identity using the one or more verification models.

Abstract: In one embodiment, a computer system stores speech data for a plurality of speakers, where the speech data includes a plurality of feature vectors and, for each feature vector, an associated sub-phonetic class. The computer system then builds, based on the speech data, an artificial neural network (ANN) for modeling speech of a target speaker in the plurality of speakers, where the ANN is configured to discriminate between instances of sub-phonetic classes uttered by the target speaker and instances of sub-phonetic classes uttered by other speakers in the plurality of speakers.

Abstract: An LPC analysis of a voice waveform is performed by an LPC analyzing circuit before analyzing vocal tract parameters by a vocal tract parameter analyzing circuit. The result of the analysis is converted into the components of source parameters by converting circuits and to eliminate the component of the source parameters from the voice waveform by an inverse filter. The components of the vocal tract parameters obtained by the analysis in the vocal tract parameter analyzing circuit is eliminated from the voice waveform through an inverse filter.