# SVVAD: Personal Voice Activity Detection for Speaker Verification

arXiv: https://arxiv.org/abs/2305.19581 · HTML: https://mlsanity.com/paper/2305.19581/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 20 of 20)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario](https://mlsanity.com/paper/2005.07272/) (2020)
- [Deep Speaker: an End-to-End Neural Speaker Embedding System](https://mlsanity.com/paper/1705.02304/) (2017)
- [rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method](https://mlsanity.com/paper/1906.03588/) (2019)
- [VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition](https://mlsanity.com/paper/2009.04323/) (2020)
- [Personal VAD: Speaker-Conditioned Voice Activity Detection](https://mlsanity.com/paper/1908.04284/) (2019)
- [Personalized Speech Enhancement: New Models and Comprehensive Evaluation](https://mlsanity.com/paper/2110.09625/) (2021)
- [Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2010.11458/) (2020)
- [Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker](https://mlsanity.com/paper/2108.03342/) (2021)
- [Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement](https://mlsanity.com/paper/2106.04129/) (2021)
- [End-to-end Neural Diarization: From Transformer to Conformer](https://mlsanity.com/paper/2106.07167/) (2021)
- [Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition](https://mlsanity.com/paper/2204.03793/) (2022)
- [The ByteDance Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2021](https://mlsanity.com/paper/2109.02047/) (2021)
- [End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models](https://mlsanity.com/paper/1809.04553/) (2018)
- [MLNET: An Adaptive Multiple Receptive-field Attention Neural Network for Voice Activity Detection](https://mlsanity.com/paper/2008.05650/) (2020)
- [Unsupervised Voice Activity Detection by Modeling Source and System Information using Zero Frequency Filtering](https://mlsanity.com/paper/2206.13420/) (2022)

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

- [Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness](https://mlsanity.com/paper/2406.09443/) (2024)
- [HyWA: Architecture-Preserving Personalized Voice Activity Detection for Full-Duplex Voice Assistants](https://mlsanity.com/paper/2510.12947/) (2025)

## Abstract (arXiv)

Voice activity detection (VAD) improves the performance of speaker verification (SV) by preserving speech segments and attenuating the effects of non-speech. However, this scheme is not ideal: (1) it fails in noisy environments or multi-speaker conversations; (2) it is trained based on inaccurate non-SV sensitive labels. To address this, we propose a speaker verification-based voice activity detection (SVVAD) framework that can adapt the speech features according to which are most informative for SV. To achieve this, we introduce a label-free training method with triplet-like losses that completely avoids the performance degradation of SV due to incorrect labeling. Extensive experiments show that SVVAD significantly outperforms the baseline in terms of equal error rate (EER) under conditions where other speakers are mixed at different ratios. Moreover, the decision boundaries reveal the importance of the different parts of speech, which are largely consistent with human judgments.