# META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR

arXiv: https://arxiv.org/abs/2409.12352 · HTML: https://mlsanity.com/paper/2409.12352/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2024.

## Built on by (most cited first, 4 of 4)

- [TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Grounding](https://mlsanity.com/paper/2601.06896/) (2026)
- [Survey of End-to-End Multi-Speaker Automatic Speech Recognition for Monaural Audio](https://mlsanity.com/paper/2505.10975/) (2025)
- [Adapting Diarization-Conditioned Whisper for End-to-End Multi-Talker Speech Recognition](https://mlsanity.com/paper/2510.03723/) (2025)
- [HEAR Who Said What: Unlocking Speaker-Attributed Reasoning via Counterfactual Voice Grounding](https://mlsanity.com/paper/2608.29120/) (2026)

## Abstract (arXiv)

We propose a novel end-to-end multi-talker automatic speech recognition (ASR) framework that enables both multi-speaker (MS) ASR and target-speaker (TS) ASR. Our proposed model is trained in a fully end-to-end manner, incorporating speaker supervision from a pre-trained speaker diarization module. We introduce an intuitive yet effective method for masking ASR encoder activations using output from the speaker supervision module, a technique we term Meta-Cat (meta-information concatenation), that can be applied to both MS-ASR and TS-ASR. Our results demonstrate that the proposed architecture achieves competitive performance in both MS-ASR and TS-ASR tasks, without the need for traditional methods, such as neural mask estimation or masking at the audio or feature level. Furthermore, we demonstrate a glimpse of a unified dual-task model which can efficiently handle both MS-ASR and TS-ASR tasks. Thus, this work illustrates that a robust end-to-end multi-talker ASR framework can be implemented with a streamlined architecture, obviating the need for the complex speaker filtering mechanisms employed in previous studies.