Nonlinear estimation of DEGG signals with applications to speech pitch detection

Abstract

Speech pitch detection remains a fundamental problem due to its importance in numerous aspects of speech processing. Current pitch detectors focus on determining the glottal closure instant (GCI). Accurate GCI measures can be obtained from the differentiated electroglottograph (DEGG) signal. Unfortunately, DEGG signals are not available in most practical applications. A novel method of pitch detection is proposed in this paper, which is based on the nonlinear estimation of DEGG signals from the acoustic speech waveform. This method requires the DEGG signals only during optimization. In operation, the proposed pitch detector marks glottal closures based strictly on the acoustic speech waveform. In addition to the algorithm development, performance comparison results are presented.