Abstract
EDHMM with decision trees is a popular model for parametric speech synthesis. Traditional training procedure constructs the decision trees after observation probability densities have been optimized with the EM algorithm, assuming the state assignment probability does not change much during tree construction. This paper proposes an iterative algorithm that removes the assumption. In the new algorithm, the decision tree construction is incorporated into the EM iteration, with a safeguard procedure that ensures convergence. Evaluation on The Boston University Radio Speech corpus shows that the proposed algorithm can achieve a significantly better optimum in the training set than the original one, and that the advantage is well generalizable to the test set.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2327-2331 |
| Number of pages | 5 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| State | Published - 2014 |
| Event | 15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, Singapore Duration: Sep 14 2014 → Sep 18 2014 |
Keywords
- Decision tree
- EM algorithm
- Speech clustering
- Speech synthesis
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modeling and Simulation