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

In this paper we describe 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 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. In this contribution we will present the combination of a microphone array together with different well known spectral normalization techniques as preprocessing stages to a Gaussian Mixture Models (GMM) based text-independent speaker recognition system. We will show that the combination of these extensively used techniques in the fields of speech enhancement and robust speaker recognition respectively, greatly improves the results obtained when the system is tested in noisy reverberant environments with short utterances from unconstrained conversational speech.