# Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free

arXiv: https://arxiv.org/abs/2207.12504 · HTML: https://mlsanity.com/paper/2207.12504/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Spot the conversation: speaker diarisation in the wild](https://mlsanity.com/paper/2007.01216/) (2020)
- [End-to-End Neural Speaker Diarization with Self-attention](https://mlsanity.com/paper/1909.06247/) (2019)
- [Speaker Diarization with LSTM](https://mlsanity.com/paper/1710.10468/) (2017)
- [End-to-End Text-Dependent Speaker Verification](https://mlsanity.com/paper/1509.08062/) (2015)
- [Joint Speech Recognition and Speaker Diarization via Sequence Transduction](https://mlsanity.com/paper/1907.05337/) (2019)
- [Fully Supervised Speaker Diarization](https://mlsanity.com/paper/1810.04719/) (2018)
- [Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detection](https://mlsanity.com/paper/1910.11646/) (2019)
- [Speech Recognition and Multi-Speaker Diarization of Long Conversations](https://mlsanity.com/paper/2005.08072/) (2020)
- [DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs](https://mlsanity.com/paper/2011.01997/) (2020)
- [TREC 2020 Podcasts Track Overview](https://mlsanity.com/paper/2103.15953/) (2021)
- [Current Challenges and Future Directions in Podcast Information Access](https://mlsanity.com/paper/2106.09227/) (2021)

## Abstract (arXiv)

Podcasts are conversational in nature and speaker changes are frequent -- requiring speaker diarization for content understanding. We propose an unsupervised technique for speaker diarization without relying on language-specific components. The algorithm is overlap-aware and does not require information about the number of speakers. Our approach shows 79% improvement on purity scores (34% on F-score) against the Google Cloud Platform solution on podcast data.