Estimation of Glottal Closing and Opening Instants in Voiced Speech Using the YAGA Algorithm

• Published in: IEEE transactions on audio, speech, and language processing Vol. 20; no. 1; pp. 82 - 91
• Main Authors: Thomas, M. R. P., Gudnason, J., Naylor, P. A.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.01.2012; Institute of Electrical and Electronics Engineers
• Subjects: Adaptation model; Algorithm design and analysis; Applied sciences; Delay; Dynamic programming; electroglottograph (EGG); Estimation; Exact sciences and technology; glottal closing instants (GCIs); glottal opening instants (GOIs); group delay function; Heuristic algorithms; Information, signal and communications theory; multiscale analysis; Signal processing; Speech; Speech processing; Telecommunications and information theory; glottal opening instants (GOIs); multiscale analysis; Acoustic signal; Speech synthesis; glottal closing instants (GCIs); Accuracy; Prosody; Vocal signal; group delay function; speech processing; Dynamic programming; Pitch(acoustics); electroglottograph (EGG); Group delay
• ISSN: 1558-7916; 1558-7924
• DOI: 10.1109/TASL.2011.2157684

Accurate estimation of glottal closing instants (GCIs) and opening instants (GOIs) is important for speech processing applications that benefit from glottal-synchronous processing including pitch tracking, prosodic speech modification, speech dereverberation, synthesis and study of pathological voice. We propose the Yet Another GCI/GOI Algorithm (YAGA) to detect GCIs from speech signals by employing multiscale analysis, the group delay function, and N -best dynamic programming. A novel GOI detector based upon the consistency of the candidates' closed quotients relative to the estimated GCIs is also presented. Particular attention is paid to the precise definition of the glottal closed phase, which we define as the analysis interval that produces minimum deviation from an all-pole model of the speech signal with closed-phase linear prediction (LP). A reference algorithm analyzing both electroglottograph (EGG) and speech signals is described for evaluation of the proposed speech-based algorithm. In addition to the development of a GCI/GOI detector, an important outcome of this work is in demonstrating that GOIs derived from the EGG signal are not necessarily well-suited to closed-phase LP analysis. Evaluation of YAGA against the APLAWD and SAM databases show that GCI identification rates of up to 99.3% can be achieved with an accuracy of 0.3 ms and GOI detection can be achieved equally reliably with an accuracy of 0.5 ms.