# Multi-resolution location-based training for multi-channel continuous speech separation

arXiv: https://arxiv.org/abs/2301.06458 · HTML: https://mlsanity.com/paper/2301.06458/

## Key facts

- Cited by 2 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 13 of its 22 references that are in the map.
- First posted on arXiv 16 Jan 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, 13 of 13)

- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [Continuous speech separation: dataset and analysis](https://mlsanity.com/paper/2001.11482/) (2020)
- [Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis](https://mlsanity.com/paper/2011.02014/) (2020)
- [Continuous Speech Separation with Conformer](https://mlsanity.com/paper/2008.05773/) (2020)
- [Voice Separation with an Unknown Number of Multiple Speakers](https://mlsanity.com/paper/2003.01531/) (2020)
- [Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation](https://mlsanity.com/paper/2010.01703/) (2020)
- [Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation](https://mlsanity.com/paper/1904.11148/) (2019)
- [Graph-PIT: Generalized permutation invariant training for continuous separation of arbitrary numbers of speakers](https://mlsanity.com/paper/2107.14446/) (2021)
- [Spatial Loss for Unsupervised Multi-channel Source Separation](https://mlsanity.com/paper/2204.00210/) (2022)
- [An Initialization Scheme for Meeting Separation with Spatial Mixture Models](https://mlsanity.com/paper/2204.01338/) (2022)
- [Separating Long-Form Speech with Group-Wise Permutation Invariant Training](https://mlsanity.com/paper/2110.14142/) (2021)
- [Continuous Speech Separation with Recurrent Selective Attention Network](https://mlsanity.com/paper/2110.14838/) (2021)

## Built on by (most cited first, 2 of 2)

- [Multi-channel Conversational Speaker Separation via Neural Diarization](https://mlsanity.com/paper/2311.08630/) (2023)
- [Position-Aware Target Speaker Extraction for Long-Form Multi-Party Conversations: A Diarization-Free Framework for ASR](https://mlsanity.com/paper/2606.29497/) (2026)

## Abstract (arXiv)

The performance of automatic speech recognition (ASR) systems severely degrades when multi-talker speech overlap occurs. In meeting environments, speech separation is typically performed to improve the robustness of ASR systems. Recently, location-based training (LBT) was proposed as a new training criterion for multi-channel talker-independent speaker separation. Assuming fixed array geometry, LBT outperforms widely-used permutation-invariant training in fully overlapped utterances and matched reverberant conditions. This paper extends LBT to conversational multi-channel speaker separation. We introduce multi-resolution LBT to estimate the complex spectrograms from low to high time and frequency resolutions. With multi-resolution LBT, convolutional kernels are assigned consistently based on speaker locations in physical space. Evaluation results show that multi-resolution LBT consistently outperforms other competitive methods on the recorded LibriCSS corpus.