Adaptive Frequency Cepstral Coefficients for Word Mispronunciation Detection
This work addresses mispronunciation detection for language learners, but it is incremental as it builds on existing HMM-based approaches with new features.
The paper tackled mispronunciation detection in language learning by proposing adaptive frequency cepstral coefficients, which improved classification rates compared to conventional methods using Mel-frequency cepstral coefficients.
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.