# Streaming Multi-Talker ASR with Token-Level Serialized Output Training

arXiv: https://arxiv.org/abs/2202.00842 · HTML: https://mlsanity.com/paper/2202.00842/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 47 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 30 of 33)

- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss](https://mlsanity.com/paper/2002.02562/) (2020)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset](https://mlsanity.com/paper/2010.11395/) (2020)
- [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018)
- [Serialized Output Training for End-to-End Overlapped Speech Recognition](https://mlsanity.com/paper/2003.12687/) (2020)
- [Continuous speech separation: dataset and analysis](https://mlsanity.com/paper/2001.11482/) (2020)
- [End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results](https://mlsanity.com/paper/1412.1602/) (2014)
- [SpecAugment on Large Scale Datasets](https://mlsanity.com/paper/1912.05533/) (2019)
- [Joint Speech Recognition and Speaker Diarization via Sequence Transduction](https://mlsanity.com/paper/1907.05337/) (2019)
- [Single-Channel Multi-Speaker Separation using Deep Clustering](https://mlsanity.com/paper/1607.02173/) (2016)
- [MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition](https://mlsanity.com/paper/1910.06522/) (2019)
- [Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers](https://mlsanity.com/paper/2006.10930/) (2020)
- [Continuous Speech Separation with Conformer](https://mlsanity.com/paper/2008.05773/) (2020)
- [Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis](https://mlsanity.com/paper/2011.02014/) (2020)
- [End-to-End Speaker-Attributed ASR with Transformer](https://mlsanity.com/paper/2104.02128/) (2021)
- [Recognizing Multi-talker Speech with Permutation Invariant Training](https://mlsanity.com/paper/1704.01985/) (2017)
- [Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone](https://mlsanity.com/paper/2103.16776/) (2021)
- [A Purely End-to-end System for Multi-speaker Speech Recognition](https://mlsanity.com/paper/1805.05826/) (2018)
- [Streaming end-to-end multi-talker speech recognition](https://mlsanity.com/paper/2011.13148/) (2020)
- [End-to-End Monaural Multi-speaker ASR System without Pretraining](https://mlsanity.com/paper/1811.02062/) (2018)
- [Recognizing Overlapped Speech in Meetings: A Multichannel Separation Approach Using Neural Networks](https://mlsanity.com/paper/1810.03655/) (2018)
- [Advances in Online Audio-Visual Meeting Transcription](https://mlsanity.com/paper/1912.04979/) (2019)
- [Streaming Multi-speaker ASR with RNN-T](https://mlsanity.com/paper/2011.11671/) (2020)
- [Semantic Mask for Transformer based End-to-End Speech Recognition](https://mlsanity.com/paper/1912.03010/) (2019)
- [Streaming Multi-talker Speech Recognition with Joint Speaker Identification](https://mlsanity.com/paper/2104.02109/) (2021)
- [Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR](https://mlsanity.com/paper/1905.12230/) (2019)
- [A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio](https://mlsanity.com/paper/2107.02852/) (2021)
- [Continuous Streaming Multi-Talker ASR with Dual-path Transducers](https://mlsanity.com/paper/2109.08555/) (2021)

## Built on by (most cited first, 30 of 37)

- [MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems](https://mlsanity.com/paper/2307.11394/) (2023)
- [The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios](https://mlsanity.com/paper/2306.13734/) (2023)
- [Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions](https://mlsanity.com/paper/2409.08596/) (2024)
- [Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systems](https://mlsanity.com/paper/2409.06656/) (2024)
- [Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings](https://mlsanity.com/paper/2203.16685/) (2022)
- [A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One](https://mlsanity.com/paper/2302.09908/) (2023)
- [One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition](https://mlsanity.com/paper/2310.01688/) (2023)
- [Empowering Whisper as a Joint Multi-Talker and Target-Talker Speech Recognition System](https://mlsanity.com/paper/2407.09817/) (2024)
- [Simulating realistic speech overlaps improves multi-talker ASR](https://mlsanity.com/paper/2210.15715/) (2022)
- [Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio](https://mlsanity.com/paper/2308.05218/) (2023)
- [Cross-Speaker Encoding Network for Multi-Talker Speech Recognition](https://mlsanity.com/paper/2401.04152/) (2024)
- [VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition](https://mlsanity.com/paper/2209.04974/) (2022)
- [Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator](https://mlsanity.com/paper/2305.16263/) (2023)
- [Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments](https://mlsanity.com/paper/2307.03354/) (2023)
- [BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR](https://mlsanity.com/paper/2305.13716/) (2023)
- [DiariST: Streaming Speech Translation with Speaker Diarization](https://mlsanity.com/paper/2309.08007/) (2023)
- [Leveraging Timestamp Information for Serialized Joint Streaming Recognition and Translation](https://mlsanity.com/paper/2310.14806/) (2023)
- [Target conversation extraction: Source separation using turn-taking dynamics](https://mlsanity.com/paper/2407.11277/) (2024)
- [LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization](https://mlsanity.com/paper/2409.00819/) (2024)
- [Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTC](https://mlsanity.com/paper/2409.12388/) (2024)
- [Alignment-Free Training for Transducer-based Multi-Talker ASR](https://mlsanity.com/paper/2409.20301/) (2024)
- [Separator-Transducer-Segmenter: Streaming Recognition and Segmentation of Multi-party Speech](https://mlsanity.com/paper/2205.05199/) (2022)
- [AG-LSEC: Audio Grounded Lexical Speaker Error Correction](https://mlsanity.com/paper/2406.17266/) (2024)
- [Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition](https://mlsanity.com/paper/2506.07515/) (2025)
- [Speaker Mask Transformer for Multi-talker Overlapped Speech Recognition](https://mlsanity.com/paper/2312.10959/) (2023)
- [TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASR](https://mlsanity.com/paper/2407.04444/) (2024)
- [Transcribing and Translating, Fast and Slow: Joint Speech Translation and Recognition](https://mlsanity.com/paper/2412.15415/) (2024)
- [Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription](https://mlsanity.com/paper/2309.08454/) (2023)
- [Hypothesis Clustering and Merging: Novel MultiTalker Speech Recognition with Speaker Tokens](https://mlsanity.com/paper/2409.15732/) (2024)
- [SEAL: Speaker Error Correction using Acoustic-conditioned Large Language Models](https://mlsanity.com/paper/2501.08421/) (2025)

## Abstract (arXiv)

This paper proposes a token-level serialized output training (t-SOT), a novel framework for streaming multi-talker automatic speech recognition (ASR). Unlike existing streaming multi-talker ASR models using multiple output branches, the t-SOT model has only a single output branch that generates recognition tokens (e.g., words, subwords) of multiple speakers in chronological order based on their emission times. A special token that indicates the change of ``virtual'' output channels is introduced to keep track of the overlapping utterances. Compared to the prior streaming multi-talker ASR models, the t-SOT model has the advantages of less inference cost and a simpler model architecture. Moreover, in our experiments with LibriSpeechMix and LibriCSS datasets, the t-SOT-based transformer transducer model achieves the state-of-the-art word error rates by a significant margin to the prior results. For non-overlapping speech, the t-SOT model is on par with a single-talker ASR model in terms of both accuracy and computational cost, opening the door for deploying one model for both single- and multi-talker scenarios.