# SC-SOT: Conditioning the Decoder on Diarized Speaker Information for End-to-End Overlapped Speech Recognition

arXiv: https://arxiv.org/abs/2506.12672 · HTML: https://mlsanity.com/paper/2506.12672/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 15 Jun 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Serialized Output Training for End-to-End Overlapped Speech Recognition](https://mlsanity.com/paper/2003.12687/) (2020)
- [End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors](https://mlsanity.com/paper/2005.09921/) (2020)
- [Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario](https://mlsanity.com/paper/2005.07272/) (2020)
- [MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition](https://mlsanity.com/paper/1910.06522/) (2019)
- [Streaming Multi-Talker ASR with Token-Level Serialized Output Training](https://mlsanity.com/paper/2202.00842/) (2022)
- [Recognizing Multi-talker Speech with Permutation Invariant Training](https://mlsanity.com/paper/1704.01985/) (2017)
- [Neural Target Speech Extraction: An Overview](https://mlsanity.com/paper/2301.13341/) (2023)
- [A Purely End-to-end System for Multi-speaker Speech Recognition](https://mlsanity.com/paper/1805.05826/) (2018)
- [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)
- [Adapting self-supervised models to multi-talker speech recognition using speaker embeddings](https://mlsanity.com/paper/2211.00482/) (2022)
- [Anatomy of Industrial Scale Multilingual ASR](https://mlsanity.com/paper/2404.09841/) (2024)
- [Target Speaker ASR with Whisper](https://mlsanity.com/paper/2409.09543/) (2024)
- [SURT 2.0: Advances in Transducer-based Multi-talker Speech Recognition](https://mlsanity.com/paper/2306.10559/) (2023)
- [Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization](https://mlsanity.com/paper/2208.13085/) (2022)
- [Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals](https://mlsanity.com/paper/2006.14150/) (2020)
- [Adaptive blind audio source extraction supervised by dominant speaker identification using x-vectors](https://mlsanity.com/paper/1910.11824/) (2019)

## Abstract (arXiv)

We propose Speaker-Conditioned Serialized Output Training (SC-SOT), an enhanced SOT-based training for E2E multi-talker ASR. We first probe how SOT handles overlapped speech, and we found the decoder performs implicit speaker separation. We hypothesize this implicit separation is often insufficient due to ambiguous acoustic cues in overlapping regions. To address this, SC-SOT explicitly conditions the decoder on speaker information, providing detailed information about "who spoke when". Specifically, we enhance the decoder by incorporating: (1) speaker embeddings, which allow the model to focus on the acoustic characteristics of the target speaker, and (2) speaker activity information, which guides the model to suppress non-target speakers. The speaker embeddings are derived from a jointly trained E2E speaker diarization model, mitigating the need for speaker enrollment. Experimental results demonstrate the effectiveness of our conditioning approach on overlapped speech.