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Syllable Segmentation of Tamil Speech Signals Using Vowel Onset Point and Spectral Transition Measure

机译:使用元音发作点和光谱过渡测量的泰米尔语音信号的音节分割

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摘要

Abstract Segmentation plays vital role in speech recognition systems. An automatic segmentation of Tamil speech into syllable has been carried out using Vowel Onset Point (VOP) and Spectral Transition Measure (STM). VOP is a phonetic event used to identify the beginning point of the vowel in speech signals. Spectral Transition Measure is performed to find the significant spectral changes in speech utterances. The performance of the proposed syllable segmentation method is measured corresponding to manual segmentation and compared with the exiting syllable method using VOP and Vowel Offset Point (VOF). The result of the experiments shows the effectiveness of the proposed system.
机译:摘要分割在语音识别系统中起着重要作用。 使用元音发作点(VOP)和光谱过渡测量(STM)进行了泰米尔语音进入音节的自动分割。 VOP是用于识别语音信号中元音的开始点的语音事件。 进行光谱转换测量以找到语音发声中的显着频谱变化。 测量所提出的音节分段方法的性能对应于手动分段,并与使用VOP和元音偏移点(VOF)的退出音节方法进行比较。 实验结果表明了所提出的系统的有效性。

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