Distortion measures for vector quantization of noisy spectrum

In this paper we address the problem of vector quantization of speech in a noisy environment. We show that the performance of a vector quantization system can be improved by adapting the distortion measure to the changing environmental conditions. The proposed method emphasizes the distortion in spectral regions where the speech signal dominates. The method functions well even when conventional pre-processor methods fail because the noise statistics cannot be estimated reliably from speech pauses (as, e.g., in tandeming operations). Objective tests confirm that the use of environmentally adaptive measures significantly improves estimation accuracy in noisy speech, while preserving the quality in the case of clean input.