A novel spectral subtraction scheme for robust speech recognition: spectral subtraction using spectral harmonics of speech

This paper addresses a novel noise-compensation scheme to solve the mismatch problem between training condition and testing condition for an automatic speech recognition (ASR) system, specifically in in-car environments. The conventional spectral subtraction schemes rely on the signal to noise ratio (SNR) such that attenuation is imposed on that part of the spectrum that appears to have low SNR, and accentuation is made on that part of high SNR. However, these schemes are based on the postulation that the power spectrum of noise is in general at the lower level in magnitude than that of speech. Therefore, while such postulation is adequate for high SNR environment, it is grossly inadequate for low SNR scenarios such as the in-car environment. This paper proposes an efficient spectral subtraction scheme focused to specifically low SNR noisy environments by distinguishing the speech-dominant segment from the noise-dominant segment in the speech spectrum. Representative experiments confirm the superior performance of the proposed method over conventional methods. The experiments are conducted using car noise-corrupted utterances of the Aurora2 corpus.