基于原子分解的应力检测与语音自动分词

Máté Ákos Tündik, B. Gerazov, Aleksandar Gjoreski, György Szaszák
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引用次数: 3

摘要

基于加权相关的原子分解(Weighted Correlation based Atom Decomposition, WCAD)是最近提出的一种生理语调模型,它将音高轮廓分解为基本成分——原子。由于这些原子被认为与喉部肌肉的激活相对应,理论上它们可以用来从音高轮廓推断出更高的语言意义。其中一个与认知信息交流相关的应用是我们评估匈牙利语的自动检测重音,这项任务相当于语音短语。结果表明,重音与音节中原子峰的存在之间以及重音和/或停顿与前一个音节中的原子谷之间存在高度相关性。我们进行了进一步的实验,以评估基于原子分解的应力检测与手工应力注释和基于隐马尔可夫模型的应力检测系统。分析表明,两种方法的结果具有可比性。
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Atom decomposition based stress detection and automatic phrasing of speech
The Weighted Correlation based Atom Decomposition (WCAD) is a recently proposed physiological intonation model that decomposes the pitch contour into elementary components - atoms. Since these atoms are said to correspond to laryngeal muscle activation, in theory they could be used to infer higher linguistic meaning from the pitch contour. One such application relevant for cognitive infocommunication is the automatic detection of stress that we evaluate on the Hungarian language, for which this task is equivalent to phonological phrasing. The results show high correlation between stress and the presence of an atom peak in a syllable, as well as between stress and/or pauses and atom valleys in the previous syllable. We have run further experiments to evaluate the atom decomposition based stress detection against hand-made stress annotations and a Hidden Markov Model based stress detection system. The analysis shows comparable results with the two methods.
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