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Motion and Depth Assisted Workload Prediction for Parallel View Synthesis Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Real-Life Voice Activity Detection Based on Audio-Visual Alignment

Authors : Jin Wang, Chao Liang, Xiaochen Wang, Zhongyuan Wang

Published in: Advances in Multimedia Information Processing -- PCM 2015

Publisher: Springer International Publishing

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Abstract

Voice activity detection (VAD) is a technology to identify whether the persons in multimedia are speaking. Most of the research efforts focused on utilizing audio and visual information to implement voice activity detection, which outperform audio or visual approach alone proposed earlier. However, current methods explore a supervised classifiers using new feature consist of audio and visual information. In the paper, we propose a novel method to detect voice activity by audio-visual alignment. Since the temporal order relationship of voice activity detection over the whole audio and visual information, we use Needleman-Wunsch algorithm to align two different sequences. Compared to existing VAD algorithms,our experimental results indicate that the proposed approach presents better results, and the accuracy rate reaches about 85 % in real-life environment.

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previous chapter Perceptual Quality Improvement for Synthesis Imaging of Chinese Spectral Radioheliograph
next chapter Emotion Recognition from EEG Signals by Leveraging Stimulus Videos
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Metadata
Title
Real-Life Voice Activity Detection Based on Audio-Visual Alignment
Authors
Jin Wang
Chao Liang
Xiaochen Wang
Zhongyuan Wang
Copyright Year
2015
Book
Advances in Multimedia Information Processing -- PCM 2015

Print ISBN: 978-3-319-24077-0

Electronic ISBN: 978-3-319-24078-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-24078-7_11