This paper models tonal variations with Stem-ML tags. Surface tone shapes often deviate from their expected canonical shapes in natural sentences, presenting a challenging case to tone modeling. In this study we employed a subset of Stem-ML tags which incorporated information of lexical tones and linguistically motivated prosodic strength of the syllable. The tags successfully captured the "distorted" tone shapes and produced contextually appropriate surface variations.
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