Abstract
This paper introduces a new approach to acoustic-phonetic modelling, the hidden dynamic model (HDM), which explicitly accounts for the coarticulation and transitions between neighbouring phones. Inspired by the fact that speech is really produced by an underlying dynamic system, the HDM consists of a single vector target per phone in a hidden dynamic space in which speech trajectories are produced by a simple dynamic system. The hidden space is mapped to the surface acoustic representation via a non-linear mapping in the form of a multilayer perceptron (MLP). Algorithms are presented for training of all the parameters (target vectors and MLP weights) from segmented and labelled acoustic observations alone, with no special initialisation. The model captures the dynamic structure of speech, and appears to aid a speech recognition task based on the SwitchBoard corpus.
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