Direct Recovery of Clean Speech Using a Hybrid Noise Suppression Algorithm for Robust Speech Recognition SystemReport as inadecuate




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ISRN Signal ProcessingVolume 2012 2012, Article ID 306305, 9 pages

Research Article

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798

School of Electronic and Information Engineering, Beihang University, China

Received 9 November 2012; Accepted 28 November 2012

Academic Editors: L. Fan and A. M. Peinado

Copyright © 2012 Peng Dai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A new log-power domain feature enhancement algorithm named NLPS is developed. It consists of two parts, direct solution of nonlinear system model and log-power subtraction. In contrast to other methods, the proposed algorithm does not need prior speech-noise statistical model. Instead, it works by direct solution of the nonlinear function derived from the speech recognition system. Separate steps are utilized to refine the accuracy of estimated cepstrum by log-power subtraction, which is the second part of the proposed algorithm. The proposed algorithm manages to solve the speech probability distribution function PDF discontinuity problem caused by traditional spectral subtraction series algorithms. The effectiveness of the proposed filter is extensively compared using the standard database, AURORA2. The results show that significant improvement can be achieved by incorporating the proposed algorithm. The proposed algorithm reaches a recognition rate of over 86% for noisy speech average from SNR 0 dB to 20 dB, which means a 48% error reduction over the baseline Mel-frequency Cepstral Coefficient MFCC system.





Author: Peng Dai, Ing Yann Soon, and Rui Tao

Source: https://www.hindawi.com/



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