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首页> 外文会议>International Conference on Information Engineering for Mechanics and Materials >Speech Endpoint Detection Based on EMD and Higher Order Statistics in Noisy Environments
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Speech Endpoint Detection Based on EMD and Higher Order Statistics in Noisy Environments

机译：基于EMD和高阶统计的语音端点检测嘈杂环境

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摘要

Accurate endpoint detection is crucial for speech recognition accuracy. This paper presents a new technique for speech endpoint detection in a noisy environment based on the empirical mode decomposition (EMD) algorithm and higher order statistics. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then higher order statistics of the IMF components can be used to extract the desired feature for endpoint detection. In order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experimental results show that the performance of the proposed algorithm is noticeable in the real speech signal tests with different SNR.
机译：准确的端点检测对于语音识别准确性至关重要。本文基于经验模式分解（EMD）算法和高阶统计，介绍了一种新的语音端点检测的新技术。利用EMD，噪声语音信号可以被分解成带有限制功能（IMF）的带限量函数的和，这是零平均AM-FM组件。然后，可用于提取IMF组件的高阶统计来提取端点检测的期望特征。为了展示所提出的方法的有效性，我们提出了示例，示出了新措施比传统措施更有效。实验结果表明，在具有不同SNR的真实语音信号测试中，所提出的算法的性能是显而易见的。

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