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Solutions for robust speech/non-speech detection in wireless environment

机译：无线环境中强大语音/非语音检测的解决方案

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摘要

The use of speech recognition systems in noisy environments requires robustness to adverse conditions. An efficient detection of speech/non-speech segments is therefore necessary. Several approaches have been proposed in order to improve the robustness of speech/non-speech detection used for speech recognition in noisy conditions. In this paper, we describe a robust speech/non-speech detection algorithm based on the estimation of noise statistics: mean and variance. Results of several experiments carried out on a database collected over the GSM network show that this new approach improves the recognizer's global performances, especially in very noisy environments. Then, spectral subtraction is used as a preprocessing technique aiming to increase the robustness to noisy conditions. We show that the improvements concern mainly noisy conditions such as calls from outside or from running cars.
机译：在嘈杂环境中使用语音识别系统需要鲁棒性对不利条件。因此需要有效地检测语音/非语音段。提出了几种方法，以改善用于语音识别的语音/非语音检测的鲁棒性。在本文中，我们描述了一种基于噪声统计估计的强大语音/非语音检测算法：均值和方差。在GSM网络上收集的数据库上进行了几个实验的结果表明，这种新方法可以提高识别器的全球性能，尤其是在非常嘈杂的环境中。然后，光谱减法用作预处理技术，其旨在将鲁棒性增加到嘈杂的条件。我们表明，改进涉及来自外部或跑车等呼叫的噪声。

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