Vowel-based Meeteilon dialect identification using a Random Forest classifier
This work addresses dialect identification for Meeteilon, a specific language, but is incremental as it applies an existing method to new data.
The paper tackled the problem of identifying three major Meeteilon dialects (Imphal, Kakching, and Sekmai) using vowel sounds, achieving an average accuracy of 61.57% with a Random Forest classifier.
This paper presents a vowel-based dialect identification system for Meeteilon. For this work, a vowel dataset is created by using Meeteilon Speech Corpora available at Linguistic Data Consortium for Indian Languages (LDC-IL). Spectral features such as formant frequencies (F1, F1 and F3) and prosodic features such as pitch (F0), energy, intensity and segment duration values are extracted from monophthong vowel sounds. Random forest classifier, a decision tree-based ensemble algorithm is used for classification of three major dialects of Meeteilon namely, Imphal, Kakching and Sekmai. Model has shown an average dialect identification performance in terms of accuracy of around 61.57%. The role of spectral and prosodic features are found to be significant in Meeteilon dialect classification.