A spectral conversion approach to the iterative Wiener filter for speech enhancement

The iterative Wiener filter (IWF) for speech enhancement in additive noise is an effective and simple algorithm to implement. One of its main disadvantages is the lack of proper criteria for convergence, which has been shown to introduce severe degradation to the estimated clean signal. Here, an improvement of the IWF algorithm is proposed, when additional information is available for the signal to be enhanced. If a small amount of clean speech data is available, spectral conversion techniques can be applied for estimating the clean short-term spectral envelope of the speech signal from the noisy signal, with significant noise reduction. Our results show an average improvement compared to the original IWF that can reach 2 dB in the segmental output signal-to-noise ratio (SNR), in low input SNRs, which is perceptually significant.