Perceptual differentiation modeling explains phoneme mispronunciation by non-native speakers

One of the difficulties in second language (L2) learning is the weakness in discriminating between acoustic diversity within an L2 phoneme category and between different categories. In this paper, we describe a general method to quantitatively measure the perceptual difference between a group of native and individual nonnative speakers. Normally, this task includes subjective listening tests and/or a thorough linguistic study. We instead use a totally automated method based on a psycho-acoustic auditory model. For a certain phoneme class, we measure the similarity of the Euclidean space spanned by the power spectrum of a native speech signal and the Euclidean space spanned by the auditory model output. We do the same for a non-native speech signal. Comparing the two similarity measurements, we find problematic phonemes for a given speaker. To validate our method, we apply it to different groups of non-native speakers of various first language (L1) backgrounds. Our results are verified by the theoretical findings in literature obtained from linguistic studies.