Locality sensitive hashing for fast computation of correlational manifold learning based feature space transformations

Manifold learning based techniques have been found to be useful for feature space transformations and semi-supervised learning in speech processing. However, the immense computational requirements in building neighborhood graphs have hindered the application of these techniques to large speech corpora. This paper presents an approach for fast computation of neighborhood graphs in the context of manifold learning. The approach, known as locality sensitive hashing (LSH), has been applied to a discriminative manifold learning based feature space transformation technique that utilizes a cosine-correlation based distance measure. Performance is evaluated first in terms computational savings at a given level of ASR performance. The results demonstrate that LSH provides a factor of 9 reduction in the computational complexity with minimal impact on speech recognition performance. A study is also performed comparing the efficiency of the LSH algorithm presented here and other LSH approaches in identifying nearest neighbors.