Abstract
In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.
This work has been partially supported by the Spanish CICyT project ALIADO, the EU integrated project CHIL and the University of Vic under the grant R0912.
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© 2005 Springer-Verlag Berlin Heidelberg
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Comas, C., Monte-Moreno, E., Solé-Casals, J. (2005). A Robust Multiple Feature Approach to Endpoint Detection in Car Environment Based on Advanced Classifiers. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_104
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DOI: https://doi.org/10.1007/11494669_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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