本论文研究复杂强背景噪声下的语音信号检测问题。

This dissertation focuses on the voice activity detection problem under adverse background noise.

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为此，首先、也是关键要解决的技术之一就是必须实现噪声环境下语音信号起点的可靠检测。

As the result, the first and one of the most important tasks is to detect the jumping-off point of speech signals reliably under noisy environments.

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在较低信噪比情况下，基于语音信号的短时相对自相关序列的短时平均幅度的端点检测能够获得较高的检测精度。

The endpoint detection based on short-time average magnitude of speech signals relative autocorrelation sequences can be detected in high accuracy under the low signal-to noise ratio.

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