Gaussian Specific Compensation for Channel Distortion in Speech Recognition

Channel distortion is one of the major factors degrading the performance of automatic speech recognition (ASR) systems. Most of the current compensation methods rely on the assumption that the channel distortion remains unchanged within an utterance or globally. However, we show in this letter that the distortion varies over speech frames even if the channel response is unchanged. To address this problem, we relax the above-mentioned assumption and propose a new method to compensate the channel distortion for each Gaussian of the acoustic models. Firstly, we derive the relationship between the clean and distorted models, and then estimate the channel magnitude response with the expectation-maximization (EM) algorithm. Finally, we obtain the matched models with the estimated magnitude response and the clean models. Experiments were conducted on the TIMIT/NTIMIT databases and the results confirmed the effectiveness of the proposed method.