Efficient manifold learning for speech recognition using locality sensitive hashing

Abstract

This paper considers the application of a random projections based hashing scheme, known as locality sensitive hashing (LSH), for fast computation of neighborhood graphs in manifold learning based feature space transformations in automatic speech recognition (ASR). Discriminative manifold learning based feature transformations have already been found to provide significant improvements in ASR performance. The motivation of this work is the fact that the high computational complexity of these techniques has prevented their application to very large speech corpora. The performance of this integrated system is evaluated both in terms of computational complexity and ASR word recognition accuracy. Further comparisons of ASR performance with the well-known linear discriminant analysis are provided. These results demonstrate that LSH provides the much needed speed boost to manifold learning techniques with minimal impact on their ASR performance, thus enabling the application of these algorithms to large speech databases.