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Speaker-Independent Vowel Recognition for Malay Children Using Time-Delay Neural Network

机译:使用时延神经网络的马来儿童独立于说话人的元音识别

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This paper investigated the speaker independent vowel recognition for Malay children using the Time Delay Neural Network (TDNN). Due to less research done on the children speech recognition, the temporal structure of the children speech was not fully understood. This 2 hidden layers TDNN was proposed to discriminate 6 Malay vowels: /a/, /e/, /∂/, /i/, /o/ and /u/. The speech database consisted of vowel sounds from 360 children speakers. Cepstral coefficient was normalized for the input of TDNN. The frame rate of the TDNN was tested with 10ms, 20ms, and 30ms. It was found out that the 30ms frame rate produced the highest vowel recognition accuracy with 81.92%. The TDNN also showed higher speech recognition rate compared to the previous studies that used Multilayer Perceptron.
机译:本文研究了使用时延神经网络(TDNN)对马来儿童的说话人独立元音识别。由于对儿童语音识别的研究较少,因此儿童语音的时间结构尚未完全了解。建议使用这2个隐藏层TDNN来区分6个马来元音:/ a /,/ e /,/∂/,/ i /,/ o /和/ u /。语音数据库包含来自360个儿童说话者的元音。倒谱系数针对TDNN的输入进行了标准化。使用10ms,20ms和30ms测试TDNN的帧速率。结果发现,30ms的帧频产生最高的元音识别精度,为81.92%。与先前使用多层感知器的研究相比,TDNN还显示出更高的语音识别率。

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