Transform-based multi-feature optimization for robust distributed speech recognition

Abstract

This paper describes a noise-robust Distributed Speech Recognition (DSR) front-end using a combination of conventional Mel-cepstral Coefficient (MFCC) and Line Spectral Frequencies (LSF). These features are adequately transformed and reduced in a multi-stream scheme using Karhunen-Loeve Transform (KLT). We investigate the performance of a new front-end DSR in terms of recognition accuracy in adverse conditions as well as in terms of dimensionality reduction. Our results showed that for highly noisy speech, the proposed transformation scheme leads to a significant improvement in recognition accuracy on Aurora 2 task.