# Multi-Speaker ASR Combining Non-Autoregressive Conformer CTC and Conditional Speaker Chain

arXiv: https://arxiv.org/abs/2106.08595 · HTML: https://mlsanity.com/paper/2106.08595/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2021.

## Built on by (most cited first, 12 of 12)

- [A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond](https://mlsanity.com/paper/2204.09269/) (2022)
- [A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One](https://mlsanity.com/paper/2302.09908/) (2023)
- [Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio](https://mlsanity.com/paper/2308.05218/) (2023)
- [Cocktail HuBERT: Generalized Self-Supervised Pre-training for Mixture and Single-Source Speech](https://mlsanity.com/paper/2303.11131/) (2023)
- [Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition](https://mlsanity.com/paper/2409.00815/) (2024)
- [BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR](https://mlsanity.com/paper/2305.13716/) (2023)
- [Serialized Output Prompting for Large Language Model-based Multi-Talker Speech Recognition](https://mlsanity.com/paper/2509.04488/) (2025)
- [End-to-end Multichannel Speaker-Attributed ASR: Speaker Guided Decoder and Input Feature Analysis](https://mlsanity.com/paper/2310.10106/) (2023)
- [Improving Speaker Assignment in Speaker-Attributed ASR for Real Meeting Applications](https://mlsanity.com/paper/2403.06570/) (2024)
- [On the Robust Approximation of ASR Metrics](https://mlsanity.com/paper/2502.12408/) (2025)
- [Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR](https://mlsanity.com/paper/2203.00232/) (2022)
- [CMT-LLM: Contextual Multi-Talker ASR Utilizing Large Language Models](https://mlsanity.com/paper/2506.12059/) (2025)

## Abstract (arXiv)

Non-autoregressive (NAR) models have achieved a large inference computation reduction and comparable results with autoregressive (AR) models on various sequence to sequence tasks. However, there has been limited research aiming to explore the NAR approaches on sequence to multi-sequence problems, like multi-speaker automatic speech recognition (ASR). In this study, we extend our proposed conditional chain model to NAR multi-speaker ASR. Specifically, the output of each speaker is inferred one-by-one using both the input mixture speech and previously-estimated conditional speaker features. In each step, a NAR connectionist temporal classification (CTC) encoder is used to perform parallel computation. With this design, the total inference steps will be restricted to the number of mixed speakers. Besides, we also adopt the Conformer and incorporate an intermediate CTC loss to improve the performance. Experiments on WSJ0-Mix and LibriMix corpora show that our model outperforms other NAR models with only a slight increase of latency, achieving WERs of 22.3% and 24.9%, respectively. Moreover, by including the data of variable numbers of speakers, our model can even better than the PIT-Conformer AR model with only 1/7 latency, obtaining WERs of 19.9% and 34.3% on WSJ0-2mix and WSJ0-3mix sets. All of our codes are publicly available at https://github.com/pengchengguo/espnet/tree/conditional-multispk.