Instance-Based Learning Algorithms
Machine Learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Contextual effects on vowel duration
Speech Communication
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
An introduction to text-to-speech synthesis
An introduction to text-to-speech synthesis
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Segmental Duration Modeling for Greek Speech Synthesis
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Bayesian networks for phone duration prediction
Speech Communication
Data-driven emotion conversion in spoken English
Speech Communication
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In the present work we address the problem of phone duration modeling for the needs of emotional speech synthesis Specifically, relying on ten well known machine learning techniques, we investigate the practical usefulness of two feature selection techniques, namely the Relief and the Correlation-based Feature Selection (CFS) algorithms, for improving the accuracy of phone duration modeling The feature selection is performed over a large set of phonetic, morphologic and syntactic features In the experiments, we employed phone duration models, based on decision trees, linear regression, lazy-learning algorithms and meta-learning algorithms, trained on a Modern Greek speech database of emotional speech, which consists of five categories of emotional speech: anger, fear, joy, neutral, sadness The experimental results demonstrated that feature selection significantly improves the accuracy of phone duration modeling regardless of the type of machine learning algorithm used for phone duration modeling.