Robust speech dereverberation based on non-negativity and sparse nature of speech spectrograms
We present an algorithm for dereverberation of speech signals for automatic speech recognition (ASR) applications. Often ASR systems are presented with speech that has been recorded in environments that include noise and reverberation. The performance of ASR systems degrades with increasing levels of noise and reverberation. While many algorithms have been proposed for robust ASR in noisy environments, reverberation is still a challenging problem. In this paper, we present <sup>1</sup> an approach for dereverberation that models reverberation as a convolution operation in the speech spectral domain. Using a least-squares error criterion we decompose reverberated spectra into clean spectra convolved with a filter. We incorporate non-negativity and sparsity of the speech spectra as constraints within a non-negative matrix factorization (NMF) framework to achieve the decomposition. In ASR experiments where the system is trained with unreverberated and reverberated speech, we show that the proposed approach can provide upto 40% and 19% relative reduction respectively in performance.