Speech enhancement using a statistically derived filter mapping

We view the speech enhancement task in two aspects: reduction of the perceptual noise level in degraded speech and reconstruction of the degraded information, which may result in improvement of speech intelligibility. We are also very interested in noiseindependent speech enhancement where test noise environments could differ in intensity from those of algorithm development. To this end, we have developed in this paper an algorithm called Noise-Independent Statistical Spectral Mapping (NISSM) to estimate a speech enhancement Wiener filter. NISSM consists of a noise-resistant transformation, which converts noisy speech to a set of noise-resistant features, and a spectral mapping function, which maps the features to autoregressive spectra of clean speech. We will show that the proposed algorithm effectively reduces noise intensity. When the noise intensity of training differs from that of testing, NISSM outperforms significantly a conventional spectral mapping. The algorithm operates frame-by-frame and is designed for real-time application. The noise interference could be stationary or non-stationary white noise with variable intensity.