Dual-domain Hierarchical Classification of Phonetic Time Series

Phonemes are the smallest units of sound produced by a human being. Automatic classification of phonemes is a well-researched topic in linguistics due to its potential for robust speech recognition. With the recent advancement of phonetic segmentation algorithms, it is now possible to generate datasets of millions of phonemes automatically. Phoneme classification on such datasets is a challenging data mining task because of the large number of classes (over a hundred) and complexities of the existing methods. In this paper, we introduce the phoneme classification problem as a data mining task. We propose a dual-domain (time and frequency) hierarchical classification algorithm. Our method uses a Dynamic Time Warping (DTW) based classifier in the top layers and time-frequency features in the lower layer. We cross-validate our method on phonemes from three online dictionaries and achieved up to 35% improvement in classification compared to existing techniques. We provide case studies on classifying accented phonemes and speaker invariant phoneme classification.