Language-Independent Speaker Classification over a Far-Field Microphone

摘要

The speaker classification approach described in this contribution leverages the analysis of both speaker and verbal content information, so as to use two light-weight components for classification: a spectral matching component based on a global representation of the entire utterance, and a temporal alignment component based on more conventional frame-level evidence. The paradigm behind the spectral matching component is related to latent semantic mapping, which postulates that the underlying structure in the data is partially obscured by the randomness of local phenomena with respect to information extraction. Uncovering this latent structure results in a parsimonious continuous parameter description of feature frames and spectral bands, which then replaces the original parameterization in clustering and identification. Such global analysis can then be advantageously combined with elementary temporal alignment. This approach has been commercially deployed for the purpose of language-independent desktop voice login over a far-field microphone.