Robust and fast vowel recognition using optimum-path forest
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The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.
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Neural networks, Pattern recognition, Signal classification, Speech recognition, Artificial neural networks, Data sets, Machine-learning, Pattern recognition techniques, Processing systems, Realtime processing, Spoken languages, State-of-the-art system, Training procedures, Training time, Vowel recognition, Biometrics, Classifiers, Computational linguistics, Information theory, Real time systems, Signal processing, Speech processing, Support vector machines, Telecommunication equipment
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Inglês
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ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, p. 2190-2193.
