# Hypothesis Clustering and Merging: Novel MultiTalker Speech Recognition with Speaker Tokens

arXiv: https://arxiv.org/abs/2409.15732 · HTML: https://mlsanity.com/paper/2409.15732/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 27 of 27)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [AudioPaLM: A Large Language Model That Can Speak and Listen](https://mlsanity.com/paper/2306.12925/) (2023)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [WHAM!: Extending Speech Separation to Noisy Environments](https://mlsanity.com/paper/1907.01160/) (2019)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks](https://mlsanity.com/paper/1703.06284/) (2017)
- [Multilingual Speech Recognition With A Single End-To-End Model](https://mlsanity.com/paper/1711.01694/) (2017)
- [Serialized Output Training for End-to-End Overlapped Speech Recognition](https://mlsanity.com/paper/2003.12687/) (2020)
- [Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks](https://mlsanity.com/paper/2012.14952/) (2020)
- [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://mlsanity.com/paper/2110.04410/) (2021)
- [Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers](https://mlsanity.com/paper/2006.10930/) (2020)
- [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)
- [A Purely End-to-end System for Multi-speaker Speech Recognition](https://mlsanity.com/paper/1805.05826/) (2018)
- [Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker Recordings](https://mlsanity.com/paper/2008.04546/) (2020)
- [Towards Universal Speech Discrete Tokens: A Case Study for ASR and TTS](https://mlsanity.com/paper/2309.07377/) (2023)
- [Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training](https://mlsanity.com/paper/1707.06527/) (2017)
- [Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition](https://mlsanity.com/paper/1906.10876/) (2019)
- [Extending Whisper with prompt tuning to target-speaker ASR](https://mlsanity.com/paper/2312.08079/) (2023)
- [Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio](https://mlsanity.com/paper/2308.05218/) (2023)
- [DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding](https://mlsanity.com/paper/2406.09345/) (2024)
- [Streaming Target-Speaker ASR with Neural Transducer](https://mlsanity.com/paper/2209.04175/) (2022)
- [Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments](https://mlsanity.com/paper/2307.03354/) (2023)
- [SA-SOT: Speaker-Aware Serialized Output Training for Multi-Talker ASR](https://mlsanity.com/paper/2403.02010/) (2024)

## Built on by (most cited first, 1 of 1)

- [Survey of End-to-End Multi-Speaker Automatic Speech Recognition for Monaural Audio](https://mlsanity.com/paper/2505.10975/) (2025)

## Abstract (arXiv)

In many real-world scenarios, such as meetings, multiple speakers are present with an unknown number of participants, and their utterances often overlap. We address these multi-speaker challenges by a novel attention-based encoder-decoder method augmented with special speaker class tokens obtained by speaker clustering. During inference, we select multiple recognition hypotheses conditioned on predicted speaker cluster tokens, and these hypotheses are merged by agglomerative hierarchical clustering (AHC) based on the normalized edit distance. The clustered hypotheses result in the multi-speaker transcriptions with the appropriate number of speakers determined by AHC. Our experiments on the LibriMix dataset demonstrate that our proposed method was particularly effective in complex 3-mix environments, achieving a 55% relative error reduction on clean data and a 36% relative error reduction on noisy data compared with conventional serialized output training.