2023年12月19日 · Abstract: 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 ...

那么如何实现VAD呢？通常思虑有两种，一种是，基于传统的DSP（一般是基于高斯统计模型，也有基于门限的hard code），另一种，基于AI模型。下面我会对这两者的发展历史和实现作一个梳理，告诉你为什么他们要这样想和业内大家都怎么做。希望大家也能抱着着

Abstract: Voice Activity Detection (VAD) is a widely used technique for separating vocal regions from audio signals, with applications in voice language coding, noise reduction, and other domains. While various strategies have been proposed to improve VAD performance, such as ACAM, DCU-10, and Tr-VAD, these approaches often suffer from common ...

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%.

2023年12月19日 · In this paper, we present the real-time semantic VAD system based on RWKV and the offline semantic VAD system based on SAN-M. Experimental results show that the semantic VAD systems outperforms the DFSMN-based system in terms of …

In this paper, we propose a novel semantic VAD for low-latency segmentation. Differ- ent from existing methods, a frame-level punctuation predic- tion task is added to the semantic VAD, and the articial end- point is included in the classication category in addition to the often-used speech presence and absence.

2018年4月9日 · Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.

2023年12月19日 · 在语音识别系统中，语音活动检测（vad）是至关重要的前端模块。 针对传统基于DFSMN的二值VAD系统噪声鲁棒性差的问题，本文进一步提出基于多任务学习的语义VAD，并针对实时和离线系统改进模型，以满足特定的应用需求。

2023年1月1日 · Voice Activity Detection (VAD) is a widely used technique for separating vocal regions from audio signals, with applications in voice language coding, noise...

2024年2月29日 · Detection cost function (DCF): The DCF is a public evaluation metric of NIST sound activity detection (SAD), which is used to measure the signal-level VAD performance in this work. In VAD segmentation, four types of decisions might appear: True Negative (TN), True Positive (TP), False Negative (FN), and False Positive (FP).