|
Título:
|
A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers
|
|
Autor/a:
|
Comas, C.; Monte-Moreno, Enric; Solé-Casals, Jordi
|
|
Otros autores:
|
Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Grup de Recerca en Tecnologies Digitals; International Work-Conference on Artificial Neural Networks (8ena : Barcelona : 2005) |
|
Notas:
|
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. |
|
Materia(s):
|
-Veu, Processament de |
|
Derechos:
|
(c) Springer, 2005
Tots els drets reservats
|
|
Tipo de documento:
|
Objeto de conferencia |
|
Editor:
|
Springer
|
|
Compartir:
|
|
