Improved Tone Recognition by Normalizing for Coarticulation and Intonation Effects

We have previously demonstrated that tone modeling improved speech recognition on a digit corpus [7]. In this work, we further improve tone recognition by normalizing for both tone coarticulation and intonation effects. The tone classification errors on continuous digit strings were reduced by 26.1% from the baseline, when the effects of F 0 downdrift, phrase boundary and tone coarticulation were normalized. We also applied the same approach to conversational speech from the YINHE domain [6], and obtained similar improvements. The word error rate on spontaneous YINHE data was reduced by 16.5% when a simple fourtone model was applied to resort recognizer 10-best outputs. 1. INTRODUCTION Tone is a natural target for prosodic modeling in tonal languages, because of its important role in lexical access. There are four lexical tones in Mandarin Chinese, each corresponding to a canonical F 0 contour pattern: "high-level", "high-rising", "low-dipping" and "high-falling". However, tones in..