DocumentCode :
835742
Title :
A quantitative assessment of group delay methods for identifying glottal closures in voiced speech
Author :
Brookes, Mike ; Naylor, Patrick A. ; Gudnason, Jon
Volume :
14
Issue :
2
fYear :
2006
fDate :
3/1/2006 12:00:00 AM
Firstpage :
456
Lastpage :
466
Abstract :
Measures based on the group delay of the LPC residual have been used by a number of authors to identify the time instants of glottal closure in voiced speech. In this paper, we discuss the theoretical properties of three such measures and we also present a new measure having useful properties. We give a quantitative assessment of each measure´s ability to detect glottal closure instants evaluated using a speech database that includes a direct measurement of glottal activity from a Laryngograph/EGG signal. We find that when using a fixed-length analysis window, the best measures can detect the instant of glottal closure in 97% of larynx cycles with a standard deviation of 0.6 ms and that in 9% of these cycles an additional excitation instant is found that normally corresponds to glottal opening. We show that some improvement in detection rate may be obtained if the analysis window length is adapted to the speech pitch. If the measures are applied to the preemphasized speech instead of to the LPC residual, we find that the timing accuracy worsens but the detection rate improves slightly. We assess the computational cost of evaluating the measures and we present new recursive algorithms that give a substantial reduction in computation in all cases.
Keywords :
data compression; recursive estimation; speech coding; glottal closures; group delay methods; laryngograph-EGG signal; linear predictive coding; quantitative assessment; recursive algorithms; voiced speech; Accuracy; Computational efficiency; Databases; Delay effects; Larynx; Linear predictive coding; Measurement standards; Speech analysis; Time measurement; Timing; Closed phase; glottal closure; group delay; speech analysis;
fLanguage :
English
Journal_Title :
Audio, Speech, and Language Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1558-7916
Type :
jour
DOI :
10.1109/TSA.2005.857810
Filename :
1597251
Link To Document :
بازگشت