Context in Multi-lingual Tone and P

Tone and intonation play a crucial role across many languages. However, the use and structure of tone varies widely, ranging from lexical tone which determines word identity to pitch accent signalling information status. In this paper, we employ a uniform representation of acoustic features for recognition of both Mandarin tone and English pitch accent. The representation captures both local tone height and shape as well as contextual coarticulatory and phrasal influences. By exploiting multiclass Support Vector Machines as a discriminative classifier, we achieve competitive rates of tone and pitch accent recognition. We further demonstrate the greater importance of modeling preceding local context, which yields up to 24% reduction in error over modeling the following context.