Tackling Perception Bias in Unsupervised Phoneme Discovery Using DPGMM-RNN Hybrid Model and Functional Load

Abstract

The human perception of phonemes is biased against speech sounds. The lack of correspondence between perceputal phonemes and acoustic signals forms a big challenge in designing unsupervised algorithms to distinguish phonemes from sound. We propose the DPGMM-RNN hybrid model that improves phoneme categorization by relieving the fragmentation problem. We also merge segments with low functional load, which is the work done by segment contrasts to differentiate between utterances, just like humans who convert unambiguous segments into phonemes as units for immediate perception. Our results show that the DPGMM-RNN hybrid model relieves the fragmentation problem and improves phoneme discriminability. The minimal functional load merge compresses a segment system, preserves information and keeps phoneme discriminability.