A robust multiple feature approach to endpoint detection in car environment based on advanced classifiers

  • Authors:

  • C. Comas;E. Monte-Moreno;J. Solé-Casals

  • Affiliations:

  • TALP Research Center, Universitat Politècnica de Catalunya, Spain;TALP Research Center, Universitat Politècnica de Catalunya, Spain;Signal Processing Group, University of Vic, Spain

  • Venue:
  • IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
  • Year:
  • 2005

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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.