Abstract
A toneme in Mandarin Chinese is a tonal phone which consists of a base phone (main vowel) and a tone. To capture both, most recognition systems use two feature streams: the standard MFCC for the base phones, and pitch features for the tones. In this paper we propose the use of dynamic Bayesian networks for modeling the two streams in toneme recognition. We used the Graphical Model Toolkit to build and compare three different models: a standard HMM with concatenated features, and synchronous and asynchronous multi-stream systems. Stream-level model parameter tying is also exploited. The toneme recognition results show significant improvements by using the multi-stream models.
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