# Advancing VAD Systems Based on Multi-Task Learning with Improved Model Structures

arXiv: https://arxiv.org/abs/2312.14860 · HTML: https://mlsanity.com/paper/2312.14860/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [A Survey of RWKV](https://mlsanity.com/paper/2412.14847/) (2024)
- [FunCineForge: A Unified Dataset Toolkit and Model for Zero-Shot Movie Dubbing in Diverse Cinematic Scenes](https://mlsanity.com/paper/2601.14777/) (2026)
- [Geometry-Aware Semantic Reasoning for Training Free Video Anomaly Detection](https://mlsanity.com/paper/2603.13374/) (2026)

## Abstract (arXiv)

In a speech recognition system, voice activity detection (VAD) is a crucial frontend module. Addressing the issues of poor noise robustness in traditional binary VAD systems based on DFSMN, the paper further proposes semantic VAD based on multi-task learning with improved models for real-time and offline systems, to meet specific application requirements. Evaluations on internal datasets show that, compared to the real-time VAD system based on DFSMN, the real-time semantic VAD system based on RWKV achieves relative decreases in CER of 7.0%, DCF of 26.1% and relative improvement in NRR of 19.2%. Similarly, when compared to the offline VAD system based on DFSMN, the offline VAD system based on SAN-M demonstrates relative decreases in CER of 4.4%, DCF of 18.6% and relative improvement in NRR of 3.5%.