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首页> 外文会议>IEEE Long Island Systems, Applications and Technology Conference >Analysis of American English corner vowels produced by Mandarin, Hindi, and American accented speakers and a baseline accent recognition system
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Analysis of American English corner vowels produced by Mandarin, Hindi, and American accented speakers and a baseline accent recognition system

机译：分析由普通话，北印度语和美国重音说话者制作的美国英语角元音和基线重音识别系统

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摘要

Individuals' speaking styles such as their accent present challenges for designing speaker independent systems and affect the performance of the systems. Accent recognition systems can be used to improve voice activated systems and Automatic Speech Recognition (ASR) systems. In this paper, we focus on analyzing American English corner vowels produced by Mandarin, Hindi and American accented speakers by using Mel-frequency cepstral coefficients (MFCCs), Linear Predictive coding (LPC), and the first two F1-F2 formant frequencies. A baseline system is designed and tested. The vowel spaces of Mandarin and Hindi accented male speakers of American English are found to be smaller compared to the female L2 speakers' vowel spaces. A significant difference among the L1 and L2 speaker groups is observed for the back corner vowel /a/. In the baseline system, MFCCs feature set achieved higher classification accuracies than the LPCs set.
机译：诸如口音之类的个人讲话风格给设计独立于扬声器的系统带来了挑战，并影响了系统的性能。口音识别系统可用于改进语音激活系统和自动语音识别（ASR）系统。在本文中，我们重点研究使用梅尔频率倒谱系数（MFCC），线性预测编码（LPC）和前两个F1-F2共振峰频率来分析由普通话，北印度语和美国重音说话者产生的美国英语元音。设计并测试了基准系统。发现与说第二语言的女性的元音空间相比，说普通话和印地语的美国英语男性发音的元音空间要小。对于后角元音/ a /，在L1和L2扬声器组之间观察到了显着差异。在基准系统中，MFCC功能集比LPC集具有更高的分类精度。

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