PROSE: Perceptual Risk Optimization for Speech Enhancement

TL;DR

This paper introduces a new risk minimization framework for speech enhancement that optimizes an unbiased estimate of distortion measures directly from noisy observations, improving denoising performance especially at higher SNRs.

Contribution

It develops a novel risk estimation approach for speech enhancement that does not require prior knowledge of clean speech statistics, using perceptually relevant distortion measures.

Findings

Outperforms traditional methods like Wiener filter and log-MMSE at SNRs above 5 dB.

Uses perceptual distortion measures such as Itakura-Saito and weighted hyperbolic cosine.

Achieves better speech quality and intelligibility in evaluations.

Abstract

The goal in speech enhancement is to obtain an estimate of clean speech starting from the noisy signal by minimizing a chosen distortion measure, which results in an estimate that depends on the unknown clean signal or its statistics. Since access to such prior knowledge is limited or not possible in practice, one has to estimate the clean signal statistics. In this paper, we develop a new risk minimization framework for speech enhancement, in which, one optimizes an unbiased estimate of the distortion/risk instead of the actual risk. The estimated risk is expressed solely as a function of the noisy observations. We consider several perceptually relevant distortion measures and develop corresponding unbiased estimates under realistic assumptions on the noise distribution and a priori signal-to-noise ratio (SNR). Minimizing the risk estimates gives rise to the corresponding denoisers, which are nonlinear functions of the a posteriori SNR. Perceptual evaluation of speech quality (PESQ), average segmental SNR (SSNR) computations, and listening tests show that the proposed risk optimization approach employing Itakura-Saito and weighted hyperbolic cosine distortions gives better performance than the other distortion measures. For SNRs greater than 5 dB, the proposed approach gives superior denoising performance over the benchmark techniques based on the Wiener filter, log-MMSE minimization, and Bayesian nonnegative matrix factorization.