Joint Far- and Near-End Speech Intelligibility Enhancement based on the Approximated Speech Intelligibility Index

TL;DR

This paper introduces a simplified approach to joint far- and near-end speech enhancement using the Approximated Speech Intelligibility Index, achieving effective results without complex modeling of speech variations.

Contribution

It proposes a closed-form solution for joint speech enhancement based on ASII, avoiding complex models and assumptions of natural speech variations.

Findings

Achieves similar performance to existing methods

Does not require modeling natural speech variations

Provides a closed-form, distribution-independent solution

Abstract

This paper considers speech enhancement of signals picked up in one noisy environment which must be presented to a listener in another noisy environment. Recently, it has been shown that an optimal solution to this problem requires the consideration of the noise sources in both environments jointly. However, the existing optimal mutual information based method requires a complicated system model that includes natural speech variations, and relies on approximations and assumptions of the underlying signal distributions. In this paper, we propose to use a simpler signal model and optimize speech intelligibility based on the Approximated Speech Intelligibility Index (ASII). We derive a closed-form solution to the joint far- and near-end speech enhancement problem that is independent of the marginal distribution of signal coefficients, and that achieves similar performance to existing work. In addition, we do not need to model or optimize for natural speech variations.