基于支持向量机的语音活动检测

M. Baig, S. Masud, Mian M. Awais
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引用次数: 8

摘要

语音活动检测是实现高效语音编码和准确自动语音识别的重要手段。过去提出的解决VAD问题的大多数算法都是基于语音信号的一些确定性特征,如过零率。然后使用适当选择的阈值进行语音/非语音决策。本文介绍了支持向量机在语音活动分类中的应用。语音信号被划分为有标记的重叠帧,然后使用监督学习算法进行模式分类。已经观察到,基于SVM的解决方案计算效率高,对于使用麦克风直接记录的语音信号,准确率约为90%,对于有噪声的语音,准确率超过85%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Support Vector Machine based Voice Activity Detection
Voice activity detection (VAD) is important for efficient speech coding and accurate automatic speech recognition (ASR). Most of the algorithms proposed in the past, for solving the VAD problem, have been based on some deterministic feature of the speech signal such as zero crossing rate. The speech/non-speech decisions are then taken using suitably chosen thresholds. This paper presents the application of support vector machines (SVM) for classifying the voice activity. The speech signal has been divided into labeled overlapping frames and pattern classification has subsequently been performed by using a supervised learning algorithm. It has been observed that the SVM based solution is computationally efficient and provides around 90% accuracy for speech signals directly recorded using a microphone and an accuracy of over 85% for noisy speech
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