# End-to-End Speaker-Attributed ASR with Transformer

arXiv: https://arxiv.org/abs/2104.02128 · HTML: https://mlsanity.com/paper/2104.02128/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 36 references that are in the map.
- First posted on arXiv 5 Apr 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [A Comparative Study on Transformer vs RNN in Speech Applications](https://mlsanity.com/paper/1909.06317/) (2019)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [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)
- [Joint Speech Recognition and Speaker Diarization via Sequence Transduction](https://mlsanity.com/paper/1907.05337/) (2019)
- [Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers](https://mlsanity.com/paper/2006.10930/) (2020)
- [Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap](https://mlsanity.com/paper/2003.02405/) (2020)
- [Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis](https://mlsanity.com/paper/2011.02014/) (2020)
- [Recognizing Multi-talker Speech with Permutation Invariant Training](https://mlsanity.com/paper/1704.01985/) (2017)
- [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)
- [Advances in Online Audio-Visual Meeting Transcription](https://mlsanity.com/paper/1912.04979/) (2019)
- [Speech Recognition and Multi-Speaker Diarization of Long Conversations](https://mlsanity.com/paper/2005.08072/) (2020)
- [Streaming Multi-speaker ASR with RNN-T](https://mlsanity.com/paper/2011.11671/) (2020)
- [Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese](https://mlsanity.com/paper/1804.10752/) (2018)
- [Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition](https://mlsanity.com/paper/1906.10876/) (2019)
- [Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models](https://mlsanity.com/paper/1909.08103/) (2019)
- [Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASR](https://mlsanity.com/paper/2011.02921/) (2020)

## Built on by (most cited first, 30 of 35)

- [Streaming Multi-Talker ASR with Token-Level Serialized Output Training](https://mlsanity.com/paper/2202.00842/) (2022)
- [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)
- [DiarizationLM: Speaker Diarization Post-Processing with Large Language Models](https://mlsanity.com/paper/2401.03506/) (2024)
- [SepTr: Separable Transformer for Audio Spectrogram Processing](https://mlsanity.com/paper/2203.09581/) (2022)
- [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)
- [A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One](https://mlsanity.com/paper/2302.09908/) (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)
- [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)
- [Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech](https://mlsanity.com/paper/2112.05863/) (2021)
- [A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings](https://mlsanity.com/paper/2203.16834/) (2022)
- [Unifying Diarization, Separation, and ASR with Multi-Speaker Encoder](https://mlsanity.com/paper/2508.20474/) (2025)
- [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)
- [Alignment-Free Training for Transducer-based Multi-Talker ASR](https://mlsanity.com/paper/2409.20301/) (2024)
- [MSA-ASR: Efficient Multilingual Speaker Attribution with frozen ASR Models](https://mlsanity.com/paper/2411.18152/) (2024)
- [Survey of End-to-End Multi-Speaker Automatic Speech Recognition for Monaural Audio](https://mlsanity.com/paper/2505.10975/) (2025)
- [Separator-Transducer-Segmenter: Streaming Recognition and Segmentation of Multi-party Speech](https://mlsanity.com/paper/2205.05199/) (2022)
- [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)
- [Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition](https://mlsanity.com/paper/2506.07515/) (2025)
- [Adapting Diarization-Conditioned Whisper for End-to-End Multi-Talker Speech Recognition](https://mlsanity.com/paper/2510.03723/) (2025)
- [Towards Word-Level End-to-End Neural Speaker Diarization with Auxiliary Network](https://mlsanity.com/paper/2309.08489/) (2023)
- [Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transfer](https://mlsanity.com/paper/2403.17327/) (2024)
- [Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning](https://mlsanity.com/paper/2404.10842/) (2024)
- [Hypothesis Clustering and Merging: Novel MultiTalker Speech Recognition with Speaker Tokens](https://mlsanity.com/paper/2409.15732/) (2024)
- [GLAD: Global-Local Aware Dynamic Mixture-of-Experts for Multi-Talker ASR](https://mlsanity.com/paper/2509.13093/) (2025)

## Abstract (arXiv)

This paper presents our recent effort on end-to-end speaker-attributed automatic speech recognition, which jointly performs speaker counting, speech recognition and speaker identification for monaural multi-talker audio. Firstly, we thoroughly update the model architecture that was previously designed based on a long short-term memory (LSTM)-based attention encoder decoder by applying transformer architectures. Secondly, we propose a speaker deduplication mechanism to reduce speaker identification errors in highly overlapped regions. Experimental results on the LibriSpeechMix dataset shows that the transformer-based architecture is especially good at counting the speakers and that the proposed model reduces the speaker-attributed word error rate by 47% over the LSTM-based baseline. Furthermore, for the LibriCSS dataset, which consists of real recordings of overlapped speech, the proposed model achieves concatenated minimum-permutation word error rates of 11.9% and 16.3% with and without target speaker profiles, respectively, both of which are the state-of-the-art results for LibriCSS with the monaural setting.