# Speaker detection in the wild: Lessons learned from JSALT 2019

arXiv: https://arxiv.org/abs/1912.00938 · HTML: https://mlsanity.com/paper/1912.00938/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Overlapped speech and gender detection with WavLM pre-trained features](https://mlsanity.com/paper/2209.04167/) (2022)
- [BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordings](https://mlsanity.com/paper/2509.15001/) (2025)
- [Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture](https://mlsanity.com/paper/2309.09180/) (2023)
- [Joint speech and overlap detection: a benchmark over multiple audio setup and speech domains](https://mlsanity.com/paper/2307.13012/) (2023)
- [Multi-microphone Automatic Speech Segmentation in Meetings Based on Circular Harmonics Features](https://mlsanity.com/paper/2306.04268/) (2023)
- [Exploring Speaker Diarization with Mixture of Experts](https://mlsanity.com/paper/2506.14750/) (2025)

## Abstract (arXiv)

This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios. The main focus was to tackle a wide range of conditions that go from meetings to wild speech. We describe the research threads we explored and a set of modules that was successful for these scenarios. The ultimate goal was to explore speaker detection; but our first finding was that an effective diarization improves detection, and not having a diarization stage impoverishes the performance. All the different configurations of our research agree on this fact and follow a main backbone that includes diarization as a previous stage. With this backbone, we analyzed the following problems: voice activity detection, how to deal with noisy signals, domain mismatch, how to improve the clustering; and the overall impact of previous stages in the final speaker detection. In this paper, we show partial results for speaker diarizarion to have a better understanding of the problem and we present the final results for speaker detection.