# Multi-turn RNN-T for streaming recognition of multi-party speech

arXiv: https://arxiv.org/abs/2112.10200 · HTML: https://mlsanity.com/paper/2112.10200/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 34 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [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)
- [SpecAugment on Large Scale Datasets](https://mlsanity.com/paper/1912.05533/) (2019)
- [Single-Channel Multi-Speaker Separation using Deep Clustering](https://mlsanity.com/paper/1607.02173/) (2016)
- [Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers](https://mlsanity.com/paper/2006.10930/) (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)
- [Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker Recordings](https://mlsanity.com/paper/2008.04546/) (2020)
- [End-to-End Monaural Multi-speaker ASR System without Pretraining](https://mlsanity.com/paper/1811.02062/) (2018)
- [Streaming Multi-speaker ASR with RNN-T](https://mlsanity.com/paper/2011.11671/) (2020)
- [Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training](https://mlsanity.com/paper/1707.06527/) (2017)
- [Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR](https://mlsanity.com/paper/2006.02786/) (2020)
- [Streaming Multi-talker Speech Recognition with Joint Speaker Identification](https://mlsanity.com/paper/2104.02109/) (2021)
- [A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio](https://mlsanity.com/paper/2107.02852/) (2021)
- [Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech](https://mlsanity.com/paper/1905.03500/) (2019)
- [End-to-end training of time domain audio separation and recognition](https://mlsanity.com/paper/1912.08462/) (2019)
- [Graph-PIT: Generalized permutation invariant training for continuous separation of arbitrary numbers of speakers](https://mlsanity.com/paper/2107.14446/) (2021)

## Built on by (most cited first, 10 of 10)

- [Streaming Multi-Talker ASR with Token-Level Serialized Output Training](https://mlsanity.com/paper/2202.00842/) (2022)
- [MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems](https://mlsanity.com/paper/2307.11394/) (2023)
- [Mixture Encoder for Joint Speech Separation and Recognition](https://mlsanity.com/paper/2306.12173/) (2023)
- [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)
- [Summary of the NOTSOFAR-1 Challenge: Highlights and Learnings](https://mlsanity.com/paper/2501.17304/) (2025)
- [End-to-end multi-talker audio-visual ASR using an active speaker attention module](https://mlsanity.com/paper/2204.00652/) (2022)
- [Utilizing Neural Transducers for Two-Stage Text-to-Speech via Semantic Token Prediction](https://mlsanity.com/paper/2401.01498/) (2024)
- [Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition](https://mlsanity.com/paper/2406.08914/) (2024)
- [Error Analysis in a Modular Meeting Transcription System](https://mlsanity.com/paper/2509.10143/) (2025)

## Abstract (arXiv)

Automatic speech recognition (ASR) of single channel far-field recordings with an unknown number of speakers is traditionally tackled by cascaded modules. Recent research shows that end-to-end (E2E) multi-speaker ASR models can achieve superior recognition accuracy compared to modular systems. However, these models do not ensure real-time applicability due to their dependency on full audio context. This work takes real-time applicability as the first priority in model design and addresses a few challenges in previous work on multi-speaker recurrent neural network transducer (MS-RNN-T). First, we introduce on-the-fly overlapping speech simulation during training, yielding 14% relative word error rate (WER) improvement on LibriSpeechMix test set. Second, we propose a novel multi-turn RNN-T (MT-RNN-T) model with an overlap-based target arrangement strategy that generalizes to an arbitrary number of speakers without changes in the model architecture. We investigate the impact of the maximum number of speakers seen during training on MT-RNN-T performance on LibriCSS test set, and report 28% relative WER improvement over the two-speaker MS-RNN-T. Third, we experiment with a rich transcription strategy for joint recognition and segmentation of multi-party speech. Through an in-depth analysis, we discuss potential pitfalls of the proposed system as well as promising future research directions.