Speech Enhancement Using Generalized Maximum A Posteriori Spectral Amplitude Estimator

This paper proposes a generalized maximum a posteriori spectral amplitude (GMAPA) algorithm to spectral restoration for speech enhancement. The proposed GMAPA algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMAPA adopts a smaller scale to prevent over-compensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMAPA uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, Aurora-4 and Aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMAPA algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), maximum likelihood spectral amplitude estimator (MLSA), and maximum a posteriori spectral amplitude estimator (MAPA). The experimental results first confirm that GMAPA provides better objective evaluation scores than MMSE, MLSA, and MAPA in lower SNR conditions, with comparable scores to MLSA in higher SNR conditions. Moreover, our recognition results indicate that GMAPA outperforms the three conventional algorithms consistently over different testing conditions.