This paper develops an online and freely available framework to aid teaching and learning the prosodic control of Tokyo Japanese: how to generate its adequate word accent and phrase intonation. This framework is called OJAD (Online Japanese Accent Dictionary) [1] and it provides three features. 1) Visual, auditory, systematic, and comprehensive illustration of patterns of accent change (accent sandhi) of verbs and adjectives. Here only the changes caused by twelve fundamental conjugations are focused upon. 2) Visual illustration of the accent pattern of a given verbal expression, which is a combination of a verb and its postpositional auxiliary words. 3) Visual illustration of the pitch pattern of any given sentence and the expected positions of accent nuclei in the sentence. The third feature is technically implemented by using an accent change prediction module that we developed for Japanese Text-To-Speech (TTS) synthesis [2],[3]. Experiments show that accent nucleus assignment to given texts by the proposed framework is much more accurate than that by native speakers. Subjective assessment and objective assessment done by teachers and learners show extremely high pedagogical effectiveness of the developed framework.

Automatic labeling of prosodic features is an important topic when constructing large speech databases for speech synthesis or analysis purposes. Perceptually-related F0 parameters are proposed with the aim of automatically classifying phrase final tones. Analyses are conducted to verify how consistently subjects are able to categorize phrase final tones, and how perceptual features are related with the categories. Three types of acoustic parameters are proposed and analyzed for representing the perceptual features related to the tone categories: one related to pitch movement within the phrase final, one related to pitch reset prior to the phrase final, and one related to the length of the phrase final. A classification tree is constructed to evaluate automatic classification of phrase final tones, resulting in 79.2% accuracy for the consistently categorized samples, using the best combination among the proposed acoustic parameters.