DocumentCode :
177758
Title :
COVAREP — A collaborative voice analysis repository for speech technologies
Author :
Degottex, Gilles ; Kane, John ; Drugman, Thomas ; Raitio, Tuomo ; Scherer, Stefan
Author_Institution :
Comput. Sci. Dept., Univ. of Crete, Heraklion, Greece
fYear :
2014
fDate :
4-9 May 2014
Firstpage :
960
Lastpage :
964
Abstract :
Speech processing algorithms are often developed demonstrating improvements over the state-of-the-art, but sometimes at the cost of high complexity. This makes algorithm reimplementations based on literature difficult, and thus reliable comparisons between published results and current work are hard to achieve. This paper presents a new collaborative and freely available repository for speech processing algorithms called COVAREP, which aims at fast and easy access to new speech processing algorithms and thus facilitating research in the field. We envisage that COVAREP will allow more reproducible research by strengthening complex implementations through shared contributions and openly available code which can be discussed, commented on and corrected by the community. Presently COVAREP contains contributions from five distinct laboratories and we encourage contributions from across the speech processing research field. In this paper, we provide an overview of the current offerings of COVAREP and also include a demonstration of the algorithms through an emotion classification experiment.
Keywords :
speech processing; COVAREP; collaborative voice analysis repository; complexity cost; emotion classification experiment; speech processing algorithm; speech processing research field; speech technology; Adaptation models; Estimation; Feature extraction; Harmonic analysis; Signal processing algorithms; Speech; Speech processing; Speech processing; glottal source; sinusoidal modeling; spectral envelope; toolkit; voice quality;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
Type :
conf
DOI :
10.1109/ICASSP.2014.6853739
Filename :
6853739
Link To Document :
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