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 Mel-frequency cepstral coefficients.