Improved automatic detection of creak

Authors:
John Kane;Thomas Drugman;Christer Gobl
Affiliations:
Phonetics and Speech Laboratory, School of Linguistic, Speech and Communication Sciences, Trinity College Dublin, Ireland;TCTS Lab, University of Mons, Belgium;Phonetics and Speech Laboratory, School of Linguistic, Speech and Communication Sciences, Trinity College Dublin, Ireland
Venue:
Computer Speech and Language
Year:
2013

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Abstract

This paper describes a new algorithm for automatically detecting creak in speech signals. Detection is made by utilising two new acoustic parameters which are designed to characterise creaky excitations following previous evidence in the literature combined with new insights from observations in the current work. In particular the new method focuses on features in the Linear Prediction (LP) residual signal including the presence of secondary peaks as well as prominent impulse-like excitation peaks. These parameters are used as input features to a decision tree classifier for identifying creaky regions. The algorithm was evaluated on a range of read and conversational speech databases and was shown to clearly outperform the state-of-the-art. Further experiments involving degradations of the speech signal demonstrated robustness to both white and babble noise, providing better results than the state-of-the-art down to at least 20dB signal to noise ratio.