The pseudo-periodicity of voiced speech can be exploited in several speech
processing applications. This requires however that the precise locations of
the Glottal Closure Instants (GCIs) are available. The focus of this paper is
the evaluation of automatic methods for the detection of GCIs directly from the
speech waveform. Five state-of-the-art GCI detection algorithms are compared
using six different databases with contemporaneous electroglottographic
recordings as ground truth, and containing many hours of speech by multiple
speakers. The five techniques compared are the Hilbert Envelope-based detection
(HE), the Zero Frequency Resonator-based method (ZFR), the Dynamic Programming
Phase Slope Algorithm (DYPSA), the Speech Event Detection using the Residual
Excitation And a Mean-based Signal (SEDREAMS) and the Yet Another GCI Algorithm
(YAGA). The efficacy of these methods is first evaluated on clean speech, both
in terms of reliabililty and accuracy. Their robustness to additive noise and
to reverberation is also assessed. A further contribution of the paper is the
evaluation of their performance on a concrete application of speech processing:
the causal-anticausal decomposition of speech. It is shown that for clean
speech, SEDREAMS and YAGA are the best performing techniques, both in terms of
identification rate and accuracy. ZFR and SEDREAMS also show a superior
robustness to additive noise and reverberation.

本篇论文评估使用自动方法从语音波形中直接检测 GCIs 的效果，比较了五种最先进的 GCI 检测算法，其中 HEP、ZFR、DYPSA、SEDREAMS 和 YAGA 在干净语音上表现最佳，在鲁棒性测试中表现较好，并在语音处理的一个具体应用中显示了 S 的卓越性能。