# End-to-end Domain-Adversarial Voice Activity Detection

arXiv: https://arxiv.org/abs/1910.10655 · HTML: https://mlsanity.com/paper/1910.10655/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap](https://mlsanity.com/paper/2102.01363/) (2021)
- [Voice activity detection in the wild: A data-driven approach using teacher-student training](https://mlsanity.com/paper/2105.04065/) (2021)
- [A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis](https://mlsanity.com/paper/2109.01537/) (2021)
- [Joint speech and overlap detection: a benchmark over multiple audio setup and speech domains](https://mlsanity.com/paper/2307.13012/) (2023)
- [Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder](https://mlsanity.com/paper/2102.09607/) (2021)
- [Incorporating VAD into ASR System by Multi-task Learning](https://mlsanity.com/paper/2103.01661/) (2021)
- [No-audio speaking status detection in crowded settings via visual pose-based filtering and wearable acceleration](https://mlsanity.com/paper/2211.00549/) (2022)
- [An Explainable Proxy Model for Multiabel Audio Segmentation](https://mlsanity.com/paper/2401.08268/) (2024)
- [REWIND Dataset: Privacy-preserving Speaking Status Segmentation from Multimodal Body Movement Signals in the Wild](https://mlsanity.com/paper/2403.01229/) (2024)
- [ASoBO: Attentive Beamformer Selection for Distant Speaker Diarization in Meetings](https://mlsanity.com/paper/2406.03251/) (2024)
- [The Newsbridge -Telecom SudParis VoxCeleb Speaker Recognition Challenge 2022 System Description](https://mlsanity.com/paper/2301.07491/) (2023)
- [Multi-microphone Automatic Speech Segmentation in Meetings Based on Circular Harmonics Features](https://mlsanity.com/paper/2306.04268/) (2023)

## Abstract (arXiv)

Voice activity detection is the task of detecting speech regions in a given audio stream or recording. First, we design a neural network combining trainable filters and recurrent layers to tackle voice activity detection directly from the waveform. Experiments on the challenging DIHARD dataset show that the proposed end-to-end model reaches state-of-the-art performance and outperforms a variant where trainable filters are replaced by standard cepstral coefficients. Our second contribution aims at making the proposed voice activity detection model robust to domain mismatch. To that end, a domain classification branch is added to the network and trained in an adversarial manner. The same DIHARD dataset, drawn from 11 different domains is used for evaluation under two scenarios. In the in-domain scenario where the training and test sets cover the exact same domains, we show that the domain-adversarial approach does not degrade performance of the proposed end-to-end model. In the out-domain scenario where the test domain is different from training domains, it brings a relative improvement of more than 10%. Finally, our last contribution is the provision of a fully reproducible open-source pipeline than can be easily adapted to other datasets.