Noise aware manifold learning for robust speech recognition

This paper considers the application of discriminative manifold learning approaches in feature analysis for automatic speech recognition (ASR). The issue of manifold learning is addressed for feature space dimensionality reduction in domains involving noise corrupted speech. The locality preserving discriminant analysis (LPDA) approach to manifold learning is investigated. This class of techniques exploits the assumption that there is a structural relationship among data vectors which can be maintained by preserving the local relationships among the transformed data vectors. The paper presents a procedure for reducing the impact of varying acoustic conditions on manifold learning. Noise aware manifold learning (NAML) is described as an approach for exploiting estimated background characteristics to define the size of the local neighborhoods used for LPDA feature space transformations. It is shown that NAML significantly reduces the speech recognition WER in a noisy speech recognition task over LPDA, particularly at low signal-to-noise ratios.