Robust and fast Vowel Recognition Using Optimum-Path Forest

• Published in: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 2190 - 2193
• Main Authors: Papa, J P, Marana, A N, Spadotto, A A, Guido, R C, Falcáo, A X
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-03-2010
• Subjects: Artificial neural networks; Automatic speech recognition; Biometrics; Machine learning; Natural languages; Neural networks; Pattern recognition; Robustness; Signal classification; Speech processing; Speech recognition; Support vector machine classification; Support vector machines
• ISBN: 9781424442959; 1424442958
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP.2010.5495695

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.