# BUT CHiME-7 system description

arXiv: https://arxiv.org/abs/2310.11921 · HTML: https://mlsanity.com/paper/2310.11921/

## Key facts

- Cited by 3 papers in the map (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 16 of 16)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [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)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [WHAM!: Extending Speech Separation to Noisy Environments](https://mlsanity.com/paper/1907.01160/) (2019)
- [TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation](https://mlsanity.com/paper/2209.03952/) (2022)
- [Pruned RNN-T for fast, memory-efficient ASR training](https://mlsanity.com/paper/2206.13236/) (2022)
- [Building and Evaluation of a Real Room Impulse Response Dataset](https://mlsanity.com/paper/1811.06795/) (2018)
- [DiPCo -- Dinner Party Corpus](https://mlsanity.com/paper/1909.13447/) (2019)
- [GPU-accelerated Guided Source Separation for Meeting Transcription](https://mlsanity.com/paper/2212.05271/) (2022)
- [DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction](https://mlsanity.com/paper/2112.13520/) (2021)
- [Leveraging Low-Distortion Target Estimates for Improved Speech Enhancement](https://mlsanity.com/paper/2110.00570/) (2021)
- [Multi-Channel Target Speaker Extraction with Refinement: The WavLab Submission to the Second Clarity Enhancement Challenge](https://mlsanity.com/paper/2302.07928/) (2023)
- [Hystoc: Obtaining word confidences for fusion of end-to-end ASR systems](https://mlsanity.com/paper/2305.12579/) (2023)

## Built on by (most cited first, 3 of 3)

- [The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization](https://mlsanity.com/paper/2407.16447/) (2024)
- [MOVER: Combining Multiple Meeting Recognition Systems](https://mlsanity.com/paper/2508.05055/) (2025)
- [Cross-Talk Speech Reduction, by Separation, for Separation](https://mlsanity.com/paper/2605.19695/) (2026)

## Abstract (arXiv)

This paper describes the joint effort of Brno University of Technology (BUT), AGH University of Krakow and University of Buenos Aires on the development of Automatic Speech Recognition systems for the CHiME-7 Challenge. We train and evaluate various end-to-end models with several toolkits. We heavily relied on Guided Source Separation (GSS) to convert multi-channel audio to single channel. The ASR is leveraging speech representations from models pre-trained by self-supervised learning, and we do a fusion of several ASR systems. In addition, we modified external data from the LibriSpeech corpus to become a close domain and added it to the training. Our efforts were focused on the far-field acoustic robustness sub-track of Task 1 - Distant Automatic Speech Recognition (DASR), our systems use oracle segmentation.