From text to speech: the MITalk system
From text to speech: the MITalk system
Contextual effects on vowel duration
Speech Communication
Characterisation of rhythmic patterns for text-to-speech synthesis
Speech Communication
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Intonation modeling for Indian languages
Computer Speech and Language
Improving phone duration modelling using support vector regression fusion
Speech Communication
Feature selection for improved phone duration modeling of greek emotional speech
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Intelligent Naïve Bayes-based approaches for Web proxy caching
Knowledge-Based Systems
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In a text-to-speech system, the duration of each phone may be predicted by a duration model. This model is usually trained using a database of phones with known durations; each phone (and the context it appears in) is characterised by a feature vector that is composed of a set of linguistic factor values. We describe the use of a graphical model - a Bayesian network - for predicting the duration of a phone, given the values for these factors. The network has one discrete variable for each of the linguistic factors and a single continuous variable for the phone's duration. Dependencies between variables (or the lack of them) are represented in the BN structure by arcs (or missing arcs) between pairs of nodes. During training, both the topology of the network and its parameters are learned from labelled data. We compare the results of the BN model with results for sums of products and CART models on the same data. In terms of the root mean square error, the BN model performs much better than both CART and SoP models. In terms of correlation coefficient, the BN model performs better than the SoP model, and as well as the CART model. A BN model has certain advantages over CART and SoP models. Training SoP models requires a high degree of expertise. CART models do not deal with interactions between factors in any explicit way. As we demonstrate, a BN model can also make accurate predictions of a phone's duration, even when the values for some of the linguistic factors are unknown.