Automatic detection of pharyngeal fricatives in cleft palate speech using acoustic features based on the vocal tract area spectrum. (July 2021) - Fu, Jia; He, Fei; Yin, Heng; He, Ling
Highlights: An automatic pharyngeal fricative detection method is proposed. A vocal tract area spectrum is proposed to represent the vocal tract using time-varying cascaded pipes. Four acoustic features based on vocal tract area spectrum are extracted to identify the pharyngeal fricative. Abstract: The pharyngeal fricative is a typical compensatory articulation disorder in cleft palate speech. It is produced by retracting the root of the tongue to the posterior pharyngeal wall to substitute for the fricatives and affricates produced in the oral cavity. People who use the pharyngeal fricative have difficulties in daily communication. Research on automatic pharyngeal fricative detection can provide aids in diagnosis for speech-language pathologists and clinical doctors. This work proposes a vocal tract area spectrum (VTAS) to represent a vocal tract model using time-varying cascaded pipes. Four acoustic features based on the VTAS (the centroid and spread (CS), peak linear deviation (PLD), relative-normal entropy (RNE), mean of the ratios' statistics (MRS)) are proposed to evaluate the differences between pharyngeal fricatives and normal speech. The CS feature is proposed to evaluate the overall shape of the vocal tract to detect whether there are abnormal gestures or movements of the articulators in speech production. The PLD and RNE features focus on the variation and complexity of each vocal tube's area during the whole pronunciation process. The MRS feature is proposed to describe the continuity of the vocal tract. To evaluate the effectiveness of these four features, pharyngeal fricative detection experiments are conducted using a pharyngeal fricative dataset. This dataset contains 1246 speech samples spoken by 50 cleft palate patients and 50 normal speakers, covering all types of initial consonants in which the pharyngeal fricative usually occurs. The detection accuracy of the pharyngeal fricative using the CS, PLD, RNE and MRS feature ranges from 80.66% to 90.21%. When using the proposed CS+PLD+RNE+MRS feature, an accuracy of 95.18% can be achieved on the pharyngeal fricative dataset.