Adaptive Frequency Cepstral Coefficients for Word Mispronunciation Detection
Systems based on automatic speech recognition (ASR) technology can provide important functionality in computer assisted language learning applications. This is a young but growing area of research motivated by the large number of students studying foreign languages. Here we propose a Hidden Markov Model (HMM)-based method to detect mispronunciations. Exploiting the specific dialog scripting employed in language learning software, HMMs are trained for different pronunciations. New adaptive features have been developed and obtained through an adaptive warping of the frequency scale prior to computing the cepstral coefficients. The optimization criterion used for the warping function is to maximize separation of two major groups of pronunciations (native and non-native) in terms of classification rate. Experimental results show that the adaptive frequency scale yields a better coefficient representation leading to higher classification rates in comparison with conventional HMMs using Melfrequency cepstral coefficients.
ASR, Frequency scale MFCC AFCC Mispro-nunciation detection
Zhenhao Ge Sudhendu R. Sharma Mark J.T. Smith
School of Electrical and Computer Engineering Purdue University, West Lafayette, Indiana, 47907, USA
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
2414-2417
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）