Automatic recognition of gemination in Japanese motivated by perceptual experiments

Abstract

For Japanese speech processing, being able to automatically recognize between geminate and singleton consonants can have many benefits. In standard recognition methods, hidden Markov Models (HMMs) are used. However, HMMs are not good at differentiating between items that are distinguished primarily by temporal differences rather than spectral differences. Also, gemination depends on the length of the sounds surrounding the consonant. Because of this, we propose the construction of a method that automatically distinguishes geminates from singletons and takes these factors into account. In order to do this, it is necessary to determine which surrounding sounds are cues and what the mechanism of human recognition is. For this, we conduct perceptual experiments to examine the relationship between surrounding sounds and primary cues. Then, using these results, we design a method that can automatically recognize gemination. We test this method on two datasets including a speaking rate database. The results attained well-outperform the HMM-based method and overall outperform the case when only the primary cue is used for recognition as well as show more robustness against speaking rate.