# Serialized Output Training for End-to-End Overlapped Speech Recognition

arXiv: https://arxiv.org/abs/2003.12687 · HTML: https://mlsanity.com/paper/2003.12687/

## Key facts

- Cited by 87 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 30 of 87)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Streaming Multi-Talker ASR with Token-Level Serialized Output Training](https://mlsanity.com/paper/2202.00842/) (2022)
- [End-to-End Speaker-Attributed ASR with Transformer](https://mlsanity.com/paper/2104.02128/) (2021)
- [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)
- [MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems](https://mlsanity.com/paper/2307.11394/) (2023)
- [Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR](https://mlsanity.com/paper/2110.03151/) (2021)
- [Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions](https://mlsanity.com/paper/2409.08596/) (2024)
- [CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations](https://mlsanity.com/paper/2404.06690/) (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)
- [Extending Whisper with prompt tuning to target-speaker ASR](https://mlsanity.com/paper/2312.08079/) (2023)
- [Streaming Multi-talker Speech Recognition with Joint Speaker Identification](https://mlsanity.com/paper/2104.02109/) (2021)
- [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)
- [Many-Speakers Single Channel Speech Separation with Optimal Permutation Training](https://mlsanity.com/paper/2104.08955/) (2021)
- [Adapting Multi-Lingual ASR Models for Handling Multiple Talkers](https://mlsanity.com/paper/2305.18747/) (2023)
- [Empowering Whisper as a Joint Multi-Talker and Target-Talker Speech Recognition System](https://mlsanity.com/paper/2407.09817/) (2024)
- [A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio](https://mlsanity.com/paper/2107.02852/) (2021)
- [Multi-turn RNN-T for streaming recognition of multi-party speech](https://mlsanity.com/paper/2112.10200/) (2021)
- [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)
- [Advancing Multi-talker ASR Performance with Large Language Models](https://mlsanity.com/paper/2408.17431/) (2024)
- [Cross-Speaker Encoding Network for Multi-Talker Speech Recognition](https://mlsanity.com/paper/2401.04152/) (2024)
- [Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition](https://mlsanity.com/paper/2408.09215/) (2024)
- [Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings](https://mlsanity.com/paper/2101.01853/) (2021)
- [Improving the Naturalness of Simulated Conversations for End-to-End Neural Diarization](https://mlsanity.com/paper/2204.11232/) (2022)
- [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)
- [Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition](https://mlsanity.com/paper/2409.00815/) (2024)

## Abstract (arXiv)

This paper proposes serialized output training (SOT), a novel framework for multi-speaker overlapped speech recognition based on an attention-based encoder-decoder approach. Instead of having multiple output layers as with the permutation invariant training (PIT), SOT uses a model with only one output layer that generates the transcriptions of multiple speakers one after another. The attention and decoder modules take care of producing multiple transcriptions from overlapped speech. SOT has two advantages over PIT: (1) no limitation in the maximum number of speakers, and (2) an ability to model the dependencies among outputs for different speakers. We also propose a simple trick that allows SOT to be executed in O(S), where S is the number of the speakers in the training sample, by using the start times of the constituent source utterances. Experimental results on LibriSpeech corpus show that the SOT models can transcribe overlapped speech with variable numbers of speakers significantly better than PIT-based models. We also show that the SOT models can accurately count the number of speakers in the input audio.