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首页> 外文期刊>Speech Communication >Robust speechon-speech detection based on LDA-derived parameter and voicing parameter for speech recognition in noisy environments
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Robust speechon-speech detection based on LDA-derived parameter and voicing parameter for speech recognition in noisy environments

机译：基于LDA派生参数和发声参数的鲁棒语音/非语音检测用于嘈杂环境中的语音识别

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摘要

Every speech recognition system contains a speechon-speech detection stage. Detected speech sequences are only passed through the speech recognition stage later on. In a very noisy environment, the noise detection stage is generally responsible for most of the recognition errors. Indeed, many detected noisy periods can be recognized as a vocabulary word. This manuscript provides solutions to improve the performance of a speechon-speech detection system in very noisy environment (for both stationary and short-time energetic noise), with an application to the France Telecom system. The improvement we propose are threefold. First, noise reduction is considered in order to reduce stationary noise effects on the speech detection system. Then, in order to decrease detections of noise characterized by brief duration and high energy, two new versions of the speechon-speech detection stage are proposed. On the one hand, a linear discriminate analysis algorithm applied to the Mel frequency cepstrum coefficients is incorporated in the speechon-speech detection algorithm. On the other hand, the use of a voicing parameter is introduced in the speechon-speech detection in order to reduce the probability of false noise detections.
机译：每个语音识别系统都包含语音/非语音检测阶段。之后，检测到的语音序列仅通过语音识别阶段。在非常嘈杂的环境中，噪声检测阶段通常是造成大多数识别错误的原因。实际上，可以将许多检测到的嘈杂时段识别为一个词汇单词。该手稿提供了解决方案，可改善在非常嘈杂的环境（固定和短时高能噪声）中语音/非语音检测系统的性能，并将其应用于法国电信系统。我们提出的改进是三方面的。首先，考虑降低噪声以减少对语音检测系统的平稳噪声影响。然后，为了减少以短持续时间和高能量为特征的噪声的检测，提出了语音/非语音检测级的两个新版本。一方面，在语音/非语音检测算法中并入了应用于梅尔频率倒谱系数的线性判别分析算法。另一方面，在语音/非语音检测中引入了语音参数的使用，以减少错误检测噪声的可能性。

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