Optimizing spectral subtraction and wiener filtering for robust speech recognition in reverberant and noisy conditions

Speech enhancement is a common approach to address the effects of degradation due to noise and channel contamination. This approach is intended to suppress unwanted signal and recover the clean speech. In this paper, we focus on two simple and low-computational methods: Wiener filtering (WF) and spectral subtraction (SS). Conventionally, these are formulated with no relation with automatic speech recognition (ASR). We propose to optimize the conventional speech enhancement technique in relation with likelihood of the acoustic model. We also exploit these simple speech enhancement techniques that are originally designed for denoising, to address reverberation as well. In the experiment with real noisy and reverberant environments, we have achieved significant improvement in recognition performance using the proposed approach.