Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification

Machine learning algorithms can be used for the prediction of nonnative sound classification based on crosslinguistic acoustic similarity. To date, very few linguistic studies have compared the classification accuracy of different algorithms. This study aims to assess how well machines align with human speech perception by assessing the ability of three machine learning algorithms, namely, linear discriminant analysis (LDA), decision tree (C5.0), and neural network (NNET), to predict the classification of second language (L2) sounds in terms of first language (L1) categories. The models were trained using the first three formants and duration of L1 vowels and fed with the same acoustic features of L2 vowels. To validate their accuracy, adult L2 speakers completed a perceptual classification task. The results indicated that NNET predicted with success the classification of all L2 vowels with the highest proportion in terms of L1 categories, while LDA and C5.0 missed only one vowel each. Furthermore, NNET exhibited superior accuracy in predicting the full range of above chance responses, followed closely by LDA. C5.0 did not meet the anticipated performance levels. The findings can hold significant implications for advancing both the theoretical and practical frameworks of speech acquisition.