# ICASSP 2023 Speech Signal Improvement Challenge

arXiv: https://arxiv.org/abs/2303.06566 · HTML: https://mlsanity.com/paper/2303.06566/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [ICASSP 2024 Speech Signal Improvement Challenge](https://mlsanity.com/paper/2401.14444/) (2024)
- [Speech Signal Improvement Using Causal Generative Diffusion Models](https://mlsanity.com/paper/2303.08674/) (2023)
- [TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network](https://mlsanity.com/paper/2505.18533/) (2025)
- [RaD-Net 2: A causal two-stage repairing and denoising speech enhancement network with knowledge distillation and complex axial self-attention](https://mlsanity.com/paper/2406.07498/) (2024)
- [How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarios](https://mlsanity.com/paper/2505.01338/) (2025)

## Abstract (arXiv)

The ICASSP 2023 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. The speech signal quality can be measured with SIG in ITU-T P.835 and is still a top issue in audio communication and conferencing systems. For example, in the ICASSP 2022 Deep Noise Suppression challenge, the improvement in the background and overall quality is impressive, but the improvement in the speech signal is not statistically significant. To improve the speech signal the following speech impairment areas must be addressed: coloration, discontinuity, loudness, reverberation, and noise. A training and test set was provided for the challenge, and the winners were determined using an extended crowdsourced implementation of ITU-T P.804's listening phase. The results show significant improvement was made across all measured dimensions of speech quality.