Glottal Closure and Opening Instant Detection from Speech Signals
This work addresses speech processing for applications like glottal source characterization, but it is incremental as it builds on existing methods like DYPSA.
The paper tackles the problem of detecting Glottal Closure and Opening Instants (GCIs and GOIs) from speech signals by proposing a two-step method involving mean-based signal computation and discontinuity detection in Linear Prediction residuals, resulting in significant improvement and better noise robustness compared to the DYPSA algorithm on the CMU ARCTIC database.
This paper proposes a new procedure to detect Glottal Closure and Opening Instants (GCIs and GOIs) directly from speech waveforms. The procedure is divided into two successive steps. First a mean-based signal is computed, and intervals where speech events are expected to occur are extracted from it. Secondly, at each interval a precise position of the speech event is assigned by locating a discontinuity in the Linear Prediction residual. The proposed method is compared to the DYPSA algorithm on the CMU ARCTIC database. A significant improvement as well as a better noise robustness are reported. Besides, results of GOI identification accuracy are promising for the glottal source characterization.