# Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness

arXiv: https://arxiv.org/abs/2406.09443 · HTML: https://mlsanity.com/paper/2406.09443/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1402.1128/) (2014)
- [Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario](https://mlsanity.com/paper/2005.07272/) (2020)
- [rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method](https://mlsanity.com/paper/1906.03588/) (2019)
- [Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy](https://mlsanity.com/paper/1802.01186/) (2018)
- [Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker](https://mlsanity.com/paper/2108.03342/) (2021)
- [Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition](https://mlsanity.com/paper/2204.03793/) (2022)
- [Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization](https://mlsanity.com/paper/2208.13085/) (2022)
- [Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction](https://mlsanity.com/paper/2210.16127/) (2022)
- [Enrollment-less training for personalized voice activity detection](https://mlsanity.com/paper/2106.12132/) (2021)
- [Cross-Channel Attention-Based Target Speaker Voice Activity Detection: Experimental Results for M2MeT Challenge](https://mlsanity.com/paper/2202.02687/) (2022)
- [Efficient Multimodal Neural Networks for Trigger-less Voice Assistants](https://mlsanity.com/paper/2305.12063/) (2023)
- [End-to-End Speaker-Dependent Voice Activity Detection](https://mlsanity.com/paper/2009.09906/) (2020)
- [SVVAD: Personal Voice Activity Detection for Speaker Verification](https://mlsanity.com/paper/2305.19581/) (2023)
- [Streaming Anchor Loss: Augmenting Supervision with Temporal Significance](https://mlsanity.com/paper/2310.05886/) (2023)

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

- [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) is a critical component in various applications such as speech recognition, speech enhancement, and hands-free communication systems. With the increasing demand for personalized and context-aware technologies, the need for effective personalized VAD systems has become paramount. In this paper, we present a comparative analysis of Personalized Voice Activity Detection (PVAD) systems to assess their real-world effectiveness. We introduce a comprehensive approach to assess PVAD systems, incorporating various performance metrics such as frame-level and utterance-level error rates, detection latency and accuracy, alongside user-level analysis. Through extensive experimentation and evaluation, we provide a thorough understanding of the strengths and limitations of various PVAD variants. This paper advances the understanding of PVAD technology by offering insights into its efficacy and viability in practical applications using a comprehensive set of metrics.