Letter-to-sound (LTS) rules play a vital role in building a speech synthesis system. In this paper, we apply various Machine Learning approaches like Classification and Regression Trees (CART), Decision Forest, forest of Artificial Neural Network (ANN) and Auto Associative Neural Networks (AANN) for LTS rules. We used these techniques mainly for Schwa deletion in Hindi. We empirically show that the LTS using Decision Forest and Forest of ANNs outperforms the previous CART and normal ANN approaches respectively, and the non discriminative learning technique of AANN could not capture the LTS rules as efficiently as discriminative techniques. We explore use of syllabic features, namely, syllabic structure, onset of the syllable, number of syllables and place of Schwa along with primary contextual features. The results showed that use of these features leads to good performance. The Decision Forest and forest of ANNs approaches yielded phone accuracy of 92.86% and 93.18% respectively using the newly incorporated features for Hindi LTS.
Cite as: Sarkar, T., Joshi, S., Pammi, S.C., Prahallad, K. (2008) LTS using decision forest of regression trees and neural networks. Proc. Interspeech 2008, 1885-1888, doi: 10.21437/Interspeech.2008-190
@inproceedings{sarkar08_interspeech,
author={Tanuja Sarkar and Sachin Joshi and Sathish Chandra Pammi and Kishore Prahallad},
title={{LTS using decision forest of regression trees and neural networks}},
year=2008,
booktitle={Proc. Interspeech 2008},
pages={1885--1888},
doi={10.21437/Interspeech.2008-190}
}