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首页> 外文会议>International Conference on Communications vol.1; 20040603-05; Bucharest(RO) >WAVELET ANALYSIS APPLIED FOR ROBUST SPEECH ENDPOINT DETECTION IN NOISY ENVIRONMENTS
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WAVELET ANALYSIS APPLIED FOR ROBUST SPEECH ENDPOINT DETECTION IN NOISY ENVIRONMENTS

机译：小波分析在嘈杂环境下鲁棒语音端点检测中的应用

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摘要

The accuracy of speech recognition systems degrades severely when the systems are operated in adverse or noisy environments. This paper studies and presents practical results on the application of a technique for robust speech endpoint detection in the presence of additive noise. The technique uses wavelet analysis as an instrument for subband decomposition in order to compute a metric that defines the criterion for endpoint detection. It is demonstrated that this metric is robust to different types of noise such as Gaussian and car noise. Experimental results are focussing on the exploration of the ability of the proposed algorithm to give correct speech boundaries decisions as a function of the type of wavelet decomposition as well as the value of signal to noise ratio. Comparing with classical endpoint detectors this approach overcomes by far all the drawbacks and it may be considered an appropriate candidate for the application in speech recognisers working in noisy environments.
机译：当系统在不利或嘈杂的环境中运行时，语音识别系统的准确性会严重下降。本文研究并提出了在存在附加噪声的情况下稳健语音端点检测技术应用的实际结果。该技术使用小波分析作为子带分解的工具，以便计算定义端点检测标准的度量。事实证明，该度量标准对不同类型的噪声（例如高斯噪声和汽车噪声）具有鲁棒性。实验结果集中在探索所提出算法根据小波分解类型以及信噪比值给出正确语音边界决策的能力方面。与经典端点检测器相比，该方法克服了所有缺点，可以认为是在嘈杂环境中工作的语音识别器中应用的合适候选者。

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