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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,...
Some recent dereverberation approaches that have been effective for automatic speech recognition (ASR) applications, model reverberation as a linear convolution operation in the spectral domain, and derive a factorization to decompose spectra of reverberated speech in to those of clean speech and room-response filter. Typically, a general non-negative matrix factorization (NMF) framework is employed...
We propose the employment of nonnegative sparse linear feature extraction as a tool for unsupervised spectral unmixing. Sparse feature extraction can be seen as a general linear unmixing approach that maps the data into a new dimensional space in which each of the components has only a limited number of non-zero values. Unlike other transforms that target decorrelation or statistical independence,...
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