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首页> 外文会议>European Conference on Speech Communication and Technology v.4; 20010903-20010907; Aalborg; DK >F0 Feature Extraction by Polynomial Regression Function for Monosyllabic Thai Tone Recognition
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F0 Feature Extraction by Polynomial Regression Function for Monosyllabic Thai Tone Recognition

机译：多项式回归函数的F0特征提取用于单音节泰语识别

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摘要

This paper presents a monosyllabic Thai tone recognition system. The system is composed of three main processes, fundamental frequency (F0) extraction from input speech signal, analysis of F0 contour for feature extraction, and classification of each tone using the extracted features. In the F0 feature extraction, the polynomial regression functions are employed to fit the segmented F0 curve where its coefficients are used as a feature vector. In tone recognition, we used the maximum a posteriori probability classifier (MAP) to classify a tone by assuming that the feature is a multidimensional Gaussian random variable. The hypothetical words used in this paper are composed of numerical words and monosyllabic Thai words. The vocabulary set is composed of the short vowel words, the long vowel words and have the effect of initial and final consonant on the shape of F0 contour. The experimental results show that by using the system as a speaker-dependent system, the maximum recognition rate is 96.20% using three-dimension feature vector. The speaker-independent recognition rates are 79.99% for male and 82.80% for female using four-dimension feature vector.
机译：本文提出了一个单音节的泰语语音识别系统。该系统由三个主要过程组成：从输入语音信号中提取基频（F0），分析F0轮廓以进行特征提取以及使用提取的特征对每个音调进行分类。在F0特征提取中，多项式回归函数用于拟合分段的F0曲线，在该曲线中，其系数用作特征向量。在音调识别中，我们使用最大后验概率分类器（MAP）通过假定特征是多维高斯随机变量来对音调进行分类。本文使用的假设单词由数字单词和单音节泰语单词组成。词汇集由短元音词，长元音词组成，对F0轮廓的形状具有初始和最终辅音的影响。实验结果表明，通过将该系统作为说话人相关系统，使用三维特征向量可以最大识别率为96.20％。使用四维特征向量的男性独立于说话人的识别率为79.99％，女性为82.80％。

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