Speech Intelligibility Enhancement Using Convolutive Non-negative Matrix Factorization with Noise Prior

Abstract

We propose a convolutive non-negative matrix factorization method to improve the intelligibility of speech signal in the context of adverse noise environment. The noise bases are prior learned with Non-negative Matrix Factorization (NMF) algorithm. A modified convolutive NMF with sparse constraint is then derived to extract speech bases from noisy speech. The divergence function is selected as an objective function to get a multiplicative update of speech base and its corresponding weight. The weights of prior learned noise bases are also updated in the update rule. Listening experiments are conducted to assess the intelligibility performance of speech synthesized using the proposed algorithm. Experimental results indicate that the proposed method is very effective to improve the intelligibility of the noisy speech in various noise contexts and it outperforms conventional algorithms.