Endpoint detection of isolated utterances based on a modified Teager energy measurement

Authors:
G. S. Ying;C. D. Mitchell;L. H. Jamieson
Affiliations:
School of Electrical Engineering, Purdue University, West Lafayette, IN;School of Electrical Engineering, Purdue University, West Lafayette, IN;School of Electrical Engineering, Purdue University, West Lafayette, IN
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Year:
1993

Citing 1
Cited 5

An improved speech detection algorithm for isolated Korean utterances

ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1

Automatic silence/unvoiced/voiced classification of speech using a modified Teager energy feature

CONTROL'05 Proceedings of the 2005 WSEAS international conference on Dynamical systems and control
Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques

Artificial Intelligence in Medicine
Voice activity detection using generalized gamma distribution

SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
A robust multiple feature approach to endpoint detection in car environment based on advanced classifiers

IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Score function for voice activity detection

NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Zero crossing rate and energy of the speech signal have been the two most widely used features for locating the endpoints of an utterance. We propose a new energy measure, based on Teager's energy algorithm. This new energy measure simplifies the process of endpoint detection. We present examples showing that the new measure is more effective than traditional measures in capturing some speech events, and present experimental results demonstrating that the measure can be used to improve the performance of endpoint detection algorithms.