Speaker verification in noisy environments with combined spectral subtraction and missing feature theory

In the framework of Gaussian mixture models (GMMs) [1], we present a new approach towards robust automatic speaker verification (SV) in adverse conditions. This new and simple approach is based on the combination of a speech enhancement using traditional spectral subtraction, and a missing feature compensation to dynamically modify the probability computations performed in GMM recognizers. The identity of spectral features missing due to noise masking is provided by the spectral subtraction algorithm. Previous works have demonstrated that the missing feature modeling method succeeds in speech recognition with some artificially generated interruptions, filtering and noises [2, 3]. In this paper, we show that this method also improves noise compensation techniques used for speaker verification in more realistic conditions.