# An audio-quality-based multi-strategy approach for target speaker extraction in the MISP 2023 Challenge

arXiv: https://arxiv.org/abs/2401.03697 · HTML: https://mlsanity.com/paper/2401.03697/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2024.

## Built on by (most cited first, 1 of 1)

- [CabinSep: IR-Augmented Mask-Based MVDR for Real-Time In-Car Speech Separation with Distributed Heterogeneous Arrays](https://mlsanity.com/paper/2509.01399/) (2025)

## Abstract (arXiv)

This paper describes our audio-quality-based multi-strategy approach for the audio-visual target speaker extraction (AVTSE) task in the Multi-modal Information based Speech Processing (MISP) 2023 Challenge. Specifically, our approach adopts different extraction strategies based on the audio quality, striking a balance between interference removal and speech preservation, which benifits the back-end automatic speech recognition (ASR) systems. Experiments show that our approach achieves a character error rate (CER) of 24.2% and 33.2% on the Dev and Eval set, respectively, obtaining the second place in the challenge.