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

arXiv: https://arxiv.org/abs/2111.07759 · HTML: https://mlsanity.com/paper/2111.07759/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 2 of 2)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [An instrumental intelligibility metric based on information theory](https://mlsanity.com/paper/1708.05132/) (2017)

## Built on by (most cited first, 2 of 2)

- [Joint Noise Reduction and Listening Enhancement for Full-End Speech Enhancement](https://mlsanity.com/paper/2203.11500/) (2022)
- [Joint Minimum Processing Beamforming and Near-end Listening Enhancement](https://mlsanity.com/paper/2309.11243/) (2023)

## Abstract (arXiv)

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.