Increasing robustness in GMM speaker recognition systems for noisy and reverberant speech with low complexity microphone arrays

Abstract

Describes the additive robustness obtained through the combined use of a first acoustic processing step based on a low-complexity microphone array followed by a spectral normalization step. Microphone arrays have been shown to provide good results in reducing different sources of acoustic degradation. However, microphone arrays produce linear filtering effects that need to be compensated in order to obtain a minimal spectral distortion. We present the combination of a microphone array together with different well-known spectral normalization techniques as pre-processing stages to a Gaussian mixture models (GMM) based text-independent speaker recognition system. We show that the combination of these extensively-used techniques in the fields of speech enhancement and robust speaker recognition greatly improves the results obtained when the system is tested in noisy reverberant environments with short utterances from unconstrained conversational speech.