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首页> 外文会议>Conference on Speech Technology and Human-Computer Dialogue >Linear interpolation of spectrotemporal excitation pattern representations for automatic speech recognition in the presence of noise
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Linear interpolation of spectrotemporal excitation pattern representations for automatic speech recognition in the presence of noise

机译：光谱仪激励模式表示的线性插值在噪声存在下自动语音识别

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This article is based on the study of new methods to improve recognition capabilities of automatic speech recognition in the presence of noise systems. Instead of trying to modify complex recognition models, the study is aimed at enhancing the input data's reliability. This is achieved through processing of the acoustic representations of speech. One of these representations, called SpectroTemporal Excitation Pattern (STEP) is used in recognition systems with missing or unreliable data. One of the ideas behind this study was to increase the glimpsing areas in the STEP representations. And, because the glimpsing algorithm requires previous knowledge of the noise, another idea was to estimate noise characteristics, and base the glimpsing areas determination on these estimations. Preliminary tests were conducted with an HMM recognition system, but this will be the object of a future study.
机译：本文基于研究新方法，以提高在存在噪声系统中的自动语音识别的识别能力。该研究旨在提高输入数据的可靠性，而不是尝试修改复杂的识别模型。这是通过处理语音的声学表示来实现的。这些表示中的一个称为光谱仪激励模式（步骤）用于丢失或不可靠的数据的识别系统中。这项研究背后的想法之一是增加阶梯表示中的瞥见区域。并且，由于瞥见算法需要先前的噪声知识，因此另一个想法是估计噪声特性，并且基于这些估计的闪烁区域确定。用嗯识别系统进行初步测试，但这将是未来研究的对象。

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