Multi-environment models based linear normalization for speech recognition in car conditions

Abstract

A multi-environment adaptation technique, based on minimum mean squared error estimation, is proposed. MEMLIN (multi-environment models based linear normalization) consists of a feature adaptation using stereo data and several basic defined environments. The target of this algorithm is to learn the difference between clean and noisy feature vectors associated to a pair of Gaussians (one for a clean model, and the other for a noisy model), for each basic environment. This knowledge, the associated Gaussians, the conditional probability between clean and noisy Gaussians, and the environment are the data used to compensate the mismatch between clean and noisy vectors. This algorithm obtains important improvements regarding other techniques that look for similar targets. The experimental results with the SpeechDat Car database shows an average improvement of more than 68%, concerning the baseline, over 7 different defined environments.