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Near-end listening enhancement by noise-inverse speech shaping

In communication systems, clean speech is often reproduced by loudspeakers and disturbed by local acoustical noise. Near-end listening enhancement (NELE) is a technique to enhance the speech intelligibility in environmental noise by adaptively preprocessing the speech based on a noise estimate. Conventional NELE-algorithms adaptively filter the speech by applying spectral gains which are determined by maximizing intelligibility measures. Usually, this leads to speech amplifications at highly disturbed frequencies to overcome masking. In this paper, a new approach is presented which shapes the speech spectrum according to the inverse of the noise power spectrum. It is based on a simple gain rule. Its advantages are a predictable spectral behavior and a fixed computational complexity, since no optimization problem with an unknown number of iterations needs to be solved. Simulations have shown that it copes with a wide range of noise types and provides a similar performance compared to conventional algorithms.