A Robust Distributed Speech Recognition in Mobile Communications

In this paper, a new noise front-end is proposed to improve the performance of Distributed Speech Recognition (DSR) system using a combination of conventional Mel-Cepstral Coefficients (MFCC) and Mel-Line Spectral Frequencies (MLSF). 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, using the proposed transformation scheme MLSF-KLT leads to a significant improvement in recognition accuracy on Aurora 2 task.