| Publisher | Microsoft | ||
|---|---|---|---|
| Format | 397.2KB PDF | Date added | 25 Aug 2004 |
| Topics | Software Engineering | ||
| Downloads | 21 | ||
This paper presents a state-space formulation of a neural-network-based hidden dynamic model of speech whose parameters are trained using an approximate EM algorithm. The training makes use of the results of an off-the-shelf formant tracker (during the vowel segments) to simplify the complex sufficient statistics that would be required in the exact EM algorithm. The trained model, consisting of the state equation for the target-directed Vocal Tract Resonance (VTR) dynamics on all classes of speech sounds (including consonant closure) and the observation equation for mapping from the VTR to acoustic measurement, is then used to recover the unobserved VTR based on Extended Kalman Filter.
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