A Fast Method for High-Resolution Voiced/Unvoiced Detection and Glottal Closure/Opening Instant Estimation of Speech

Abstract

Abstract-We propose a fast speech analysis method which simultaneously performs high-resolution voiced/unvoiced detection (VUD) and accurate estimation of glottal closure and glottal opening instants (GCIs and GOIs, respectively). The proposed algorithm exploits the structure of the glottal flow derivative in order to estimate GCIs and GOIs only in voiced speech using simple time-domain criteria. We compare our method with well-known GCI/GOI methods, namely, the dynamic programming projected phase-slope algorithm (DYPSA), the yet another GCI/GOI algorithm (YAGA) and the speech event detection using the residual excitation and a mean-based signal (SEDREAMS). Furthermore, we examine the performance of the aforementioned methods when combined with state-of-the-art VUD algorithms, namely, the robust algorithm for pitch tracking (RAPT) and the summation of residual harmonics (SRH). Experiments conducted on the APLAWD and SAM databases show that the proposed algorithm outperforms the state-of-the-art combinations of VUD and GCI/GOI algorithms with respect to almost all evaluation criteria for clean speech. Experiments on speech contaminated with several noise types (white Gaussian, babble, and car-interior) are also presented and discussed. The proposed algorithm outperforms the state-of-the-art combinations in most evaluation criteria for signal-to-noise ratio greater than 10 dB.Index Terms-Glottal closure instants (GCIs), glottal opening instants (GOIs), pitch estimation, speech analysis, voiced/unvoiced detection (VUD).

Identification of glottal instants using electroglottographic signal for vulnerable cases of voicing

“…In state of the art, the researchers had proposed many methods and algorithms to determine the instants from EGG [1, 2, 9] and speech/acoustic signal [3–5, 10–13]. The main disadvantages of the existing methods were less accuracy in detection of GCIs and false detection of GOIs for the vulnerable cases of voicing.…”

A Quantitative Comparison of Epoch Extraction Algorithms for Telephone Speech

“…From the studies in [18], it was observed that most of the epoch detection methods were shown to provide good accuracy on the speech data collected in the lab environments. Also, some attempts were made to see the effectiveness of these methods for additive noise degraded conditions [19][20][21][22][23]. However, there are not many attempts in GCI detection for the degraded conditions like telephone quality speech.…”

“…YAGA is one such method [30], which uses the glottal flow waveform, wavelet transform, group delay and dynamic programming. Also, recently a method was proposed which uses the glottal flow waveform signal with the time domain criteria for detecting GCIs by forward-backward algorithm in [20].…”

Glottal Closure Instants Detection from Speech Signal by Deep Features Extracted from Raw Speech and Linear Prediction Residual

“…We can broadly (not exhaustively) classify the available GCI detection methods into i) classical signal processing [20,21,22,23,2,24,25,26] and ii) most recent classification based data driven [4,27,28] approaches. Most of the popular signal processing GCI detection methods relies on designing signal processing pipelines to obtain the exemplary signal which emphasizes the locations of GCIs in the speech signal [28].…”

“…Further, GCIs from the exemplary signal is obtained from hand-crafted heuristics. Two approaches are popular for exemplary signal extraction: a) source/filter modeling to extract the linear prediction residual whose peaks corresponds to the candidate epochs [20,21,22,23]. b) Other methods which rely on the properties of excitation signal such as impulse nature [2,24,25,26] to obtain the exemplary signal.…”