SNR-dependent waveform processing for improving the robustness of ASR front-end

Abstract

We introduce a new concept in advancing the noise robustness of a speech recognition front-end. The presented method, called SNR-dependent waveform processing (SWP), exploits SNR variability within a speech period for enhancing the high SNR period portion and attenuating the low SNR period portion in the waveform time domain. In this way, the overall SNR of noisy-speech is increased, and at the same time, the periodicity of voiced speech is enhanced. This approach differs significantly from the well-known speech enhancement techniques, which are mostly frequency domain based, and we use it in this work as a complementary technique to them. In tests with SWP, we present significant clean and noisy speech recognition performance gains using the AURORA 2 database and recognition system as defined by ETSI for the robust frontend standardization process. Moreover, the presented algorithm is very simple and it is attractive also in terms of computational load.