A Model-Based Learning Process for Modeling Coarticulation of Human Speech

Jianguo WEI  Xugang LU  Jianwu DANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E90-D   No.10   pp.1582-1591
Publication Date: 2007/10/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e90-d.10.1582
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Knowledge, Information and Creativity Support System)
Category: 
Keyword: 
machine learning,  optimization,  human mechanism,  speech,  physiological articulatory model,  

Full Text: PDF>>
Buy this Article




Summary: 
Machine learning techniques have long been applied in many fields and have gained a lot of success. The purpose of learning processes is generally to obtain a set of parameters based on a given data set by minimizing a certain objective function which can explain the data set in a maximum likelihood or minimum estimation error sense. However, most of the learned parameters are highly data dependent and rarely reflect the true physical mechanism that is involved in the observation data. In order to obtain the inherent knowledge involved in the observed data, it is necessary to combine physical models with learning process rather than only fitting the observations with a black box model. To reveal underlying properties of human speech production, we proposed a learning process based on a physiological articulatory model and a coarticulation model, where both of the models are derived from human mechanisms. A two-layer learning framework was designed to learn the parameters concerned with physiological level using the physiological articulatory model and the parameters in the motor planning level using the coarticulation model. The learning process was carried out on an articulatory database of human speech production. The learned parameters were evaluated by numerical experiments and listening tests. The phonetic targets obtained in the planning stage provided an evidence for understanding the virtual targets of human speech production. As a result, the model based learning process reveals the inherent mechanism of the human speech via the learned parameters with certain physical meaning.