# ASoBO: Attentive Beamformer Selection for Distant Speaker Diarization in Meetings

arXiv: https://arxiv.org/abs/2406.03251 · HTML: https://mlsanity.com/paper/2406.03251/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [End-to-End Neural Speaker Diarization with Self-attention](https://mlsanity.com/paper/1909.06247/) (2019)
- [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)
- [M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge](https://mlsanity.com/paper/2110.07393/) (2021)
- [AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario](https://mlsanity.com/paper/2104.03603/) (2021)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detection](https://mlsanity.com/paper/1910.11646/) (2019)
- [NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription](https://mlsanity.com/paper/2401.08887/) (2024)
- [Encoder-Decoder Based Attractors for End-to-End Neural Diarization](https://mlsanity.com/paper/2106.10654/) (2021)
- [End-to-end Domain-Adversarial Voice Activity Detection](https://mlsanity.com/paper/1910.10655/) (2019)
- [Analysis of the BUT Diarization System for VoxConverse Challenge](https://mlsanity.com/paper/2010.11718/) (2020)
- [GPU-accelerated Guided Source Separation for Meeting Transcription](https://mlsanity.com/paper/2212.05271/) (2022)
- [Overlapped speech and gender detection with WavLM pre-trained features](https://mlsanity.com/paper/2209.04167/) (2022)
- [Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition](https://mlsanity.com/paper/2109.04783/) (2021)
- [Learning Filterbanks for End-to-End Acoustic Beamforming](https://mlsanity.com/paper/2111.04614/) (2021)
- [Channel-Combination Algorithms for Robust Distant Voice Activity and Overlapped Speech Detection](https://mlsanity.com/paper/2402.08312/) (2024)

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

- [Neural Multichannel Distant Speaker Diarization with Heavy-tailed Source Separation Model](https://mlsanity.com/paper/2609.12154/) (2026)

## Abstract (arXiv)

Speaker Diarization (SD) aims at grouping speech segments that belong to the same speaker. This task is required in many speech-processing applications, such as rich meeting transcription. In this context, distant microphone arrays usually capture the audio signal. Beamforming, i.e., spatial filtering, is a common practice to process multi-microphone audio data. However, it often requires an explicit localization of the active source to steer the filter. This paper proposes a self-attention-based algorithm to select the output of a bank of fixed spatial filters. This method serves as a feature extractor for joint Voice Activity (VAD) and Overlapped Speech Detection (OSD). The speaker diarization is then inferred from the detected segments. The approach shows convincing distant VAD, OSD, and SD performance, e.g. 14.5% DER on the AISHELL-4 dataset. The analysis of the self-attention weights demonstrates their explainability, as they correlate with the speaker's angular locations.