Paper Info
Title
Burst detection based on measurements of intensity discrimination
Abstract
Detection of burst-related impulses, such as those accompanying plosive stop consonants, is an important problem for accurate measurement of acoustic features for recogntion (e.g., voice-onset-time) and for accurate automatic phonetic alignment. The proposed method of burst detection utilizes techniques for identifying and combining information about specific acoustic characteristics of bursts. One key element of the proposed method is the use of a measurement of intensity discrimination based on models from perceptual studies. Our experiments compared the proposed method of burst detection to the support vector machine (SVM) method, described below. The total error rate for the proposed method is 13.2% on the test-set partition of the TIMIT corpus, compared to a total error rate of 24% for the SVM method.
Year
Venue
Keywords
2000
INTERSPEECH
voice onset time,support vector machine,error rate
Field
DocType
Citations 
TIMIT,Intensity discrimination,Pattern recognition,Computer science,Support vector machine,Speech recognition,Artificial intelligence,Total error rate
Conference
2
PageRank 
References 
Authors
0.45
2
2
Name
Order
Citations
PageRank
John-Paul Hosom123123.43
Ronald A. Cole2686187.46