Speaker recognition using deep learning neural network

Abstract

A speaker identification/verification system comprises at least one feature extractor for extracting a plurality of audio features from speaker voice data, a plurality of speaker-specific subsystems, and a decision module. Each of the speaker-specific subsystem comprises: a neural network configured to generate an estimate of the plurality of extracted audio features based on the plurality of extracted audio features, and an error module. Each of the plurality of neural networks is associated with one of a plurality of speakers, and the one speaker associated with each of the plurality of neural networks is different for all neural networks. The error module is configured to estimate an error based on the plurality of extracted audio features and the estimate of the plurality of extracted audio features generated by the associated neural network. The neural networks are speaker-specific auto-encoders trained for one user and therefore calibrated on that particular user's speech. As a result, that speaker-specific neural network is highly tuned for the particular user and out of tune for all other users. Thus, the error associated with the speaker-specific neural network is relatively small and useful for purposes of identification or verification.

Dependencies are inferred from the claim text.