# NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge

arXiv: https://arxiv.org/abs/2409.05554 · HTML: https://mlsanity.com/paper/2409.05554/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2024.

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

- [Unifying Diarization, Separation, and ASR with Multi-Speaker Encoder](https://mlsanity.com/paper/2508.20474/) (2025)
- [Summary of the NOTSOFAR-1 Challenge: Highlights and Learnings](https://mlsanity.com/paper/2501.17304/) (2025)
- [Exploring Speaker Diarization with Mixture of Experts](https://mlsanity.com/paper/2506.14750/) (2025)
- [CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR](https://mlsanity.com/paper/2601.22792/) (2026)
- [Who Spoke What When? Evaluating Spoken Language Models for Conversational ASR with Semantic and Overlap-Aware Metrics](https://mlsanity.com/paper/2603.22709/) (2026)

## Abstract (arXiv)

We present a distant automatic speech recognition (DASR) system developed for the CHiME-8 DASR track. It consists of a diarization first pipeline. For diarization, we use end-to-end diarization with vector clustering (EEND-VC) followed by target speaker voice activity detection (TS-VAD) refinement. To deal with various numbers of speakers, we developed a new multi-channel speaker counting approach. We then apply guided source separation (GSS) with several improvements to the baseline system. Finally, we perform ASR using a combination of systems built from strong pre-trained models. Our proposed system achieves a macro tcpWER of 21.3 % on the dev set, which is a 57 % relative improvement over the baseline.