Improving the robustness of wavelet transform for epoch detection

This paper investigates (1) the robustness of epoch detection (i.e. identification of glottal closure) by the wavelet transform and (2) the methods to improve its robustness. We achieved a similar identification performance (2% error rate) to earlier investigation using the spline wavelet transform, under Gaussian noise degradation. However, the performance under other types of noise degradation, such as periodic noise (e.g. traffic lights) and short noise (e.g. keyboard noise), is not as robust as before. The scale matching technique could not secure good performance because the spline wavelet has poor recall performance. We explored the use of the Gaussian wavelet transform. Instead of scale matching, a single level is used and the recall of epochs associated with the nearest laryngograph differences by the Gaussian wavelet is about 30% more than by the spline wavelet, across different types of noise degradation. However, the spline wavelet has less false alarm (29% on average) in identification and the peaks correspond well to epoch positions (with less [standard] deviation). We evaluated detection schemes using both scalograms of Gaussian and spline wavelets and achieved improvement of recall (26%), with a relative position consistency of 1.4 ms.