Comparison Of The Prediction Accuracy Of Machine Learning Algorithms In Crosslinguistic Vowel Classification

This study aimed 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. 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. The findings can hold significant implications for advancing both the theoretical and practical frameworks of speech acquisition.