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Vowel Classification Based on Waveform Shapes

机译：基于波形形状的元音分类

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摘要

Vowel classification is an essential part of speech recognition. In classical studies, this problem is mostly handled by using spectral domain features. In this study, a novel approach is proposed for vowel classification based on the visual features of speech waveforms. In sound vocalizing, the position of certain organs of the human vocal system such as tongue, lips and jaw is very effective on the waveform shapes of the produced sound. The motivation to employ visual features instead of classical frequency domain features is its potential usage in specific applications like language education. Even though this study is confined to Turkish vowels, the developed method can be applied to other languages as well since the shapes of the vowels show similar patterns. Turkish vowels are grouped into five categories. For each vowel group, a time domain speech waveform with an interval of two pitch periods is handled as an image. A series of morphological operations is performed on this speech waveform image to obtain the geometric characteristics representing the shape of each class. The extracted visual features are then fed into three different classifiers. The classification performances of these features are compared with classical methods. It is observed that the proposed visual features achieve promising classification rates.
机译：元音分类是语音识别的重要部分。在经典研究中，此问题主要通过使用频谱域特征来解决。在这项研究中，提出了一种基于语音波形的视觉特征的元音分类的新方法。在发声时，人类发声系统的某些器官（例如舌头，嘴唇和下巴）的位置对于产生的声音的波形形状非常有效。采用视觉特征而不是经典频域特征的动机是其在诸如语言教育之类的特定应用中的潜在用途。即使这项研究仅限于土耳其语元音，但由于元音的形状显示出相似的样式，因此开发的方法也可以应用于其他语言。土耳其语元音分为五个类别。对于每个元音组，将以两个音调周期为间隔的时域语音波形作为图像处理。在该语音波形图像上执行一系列的形态学运算以获得代表每个类别的形状的几何特征。然后将提取的视觉特征输入三个不同的分类器中。将这些特征的分类性能与经典方法进行比较。观察到，提出的视觉特征实现了有希望的分类率。

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