Automatic detection of pharyngeal fricatives in cleft palate speech using acoustic features based on the vocal tract area spectrum

2021 ◽  
Vol 68 ◽  
pp. 101203
Author(s):  
Jia Fu ◽  
Fei He ◽  
Heng Yin ◽  
Ling He
Keyword(s):  
Cleft Palate ◽  
Vocal Tract ◽  
Automatic Detection ◽  
Area Spectrum ◽  
Acoustic Features ◽  
Cleft Palate Speech

Detection and assessment of hypernasality in repaired cleft palate speech using vocal tract and residual features

2019 ◽  
Vol 146 (6) ◽  
pp. 4211-4223
Author(s):  
Akhilesh Kumar Dubey ◽  
S. R. Mahadeva Prasanna ◽  
S. Dandapat
Keyword(s):  
Cleft Palate ◽  
Vocal Tract ◽  
Cleft Palate Speech

scholarly journals Automatic detection of pharyngeal fricative in cleft palate speech

2018 ◽  
Vol 246 ◽  
pp. 03007
Author(s):  
Fei He ◽  
Geyi Zhou ◽  
Xinyi He ◽  
Heng Yin ◽  
Ling He
Keyword(s):  
Cleft Palate ◽  
Nearest Neighbor ◽  
Automatic Detection ◽  
Acoustic Parameter ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Acoustic Feature ◽  
Nearest Neighbor Classifier ◽  
Cleft Palate Speech ◽  
Neighbor Classifier

Pharyngeal fricative occurs during the production of consonants, which makes the consonants lose or weaken in cleft palate speech. In clinical application, the automatic detection of pharyngeal fricative in cleft palate speech could provide objective and effective assistant aids for speech language pathologists. In this paper, a novel acoustic parameter is proposed to detect the existence of pharyngeal fricative in cleft palate speech. This proposed acoustic feature ICPD (Independent Consonant Prominent Distribution) reflects the movement of mouth and tongue. The experimental results show that normal fricative has the higher ICPD. The extracted ICPD feature is combined with k-nearest neighbor classifier to achieve the automatic detection of pharyngeal fricative. The proposed system is tested on 127 speech samples recorded by cleft palate patients and 94 by normal speakers of controls. The overall pharyngeal fricative detection accuracy is around 90%.

Automatic detection of glottal stop in cleft palate speech

2018 ◽  
Vol 39 ◽  
pp. 230-236 ◽  
Author(s):  
Ling He ◽  
Jing Zhang ◽  
Qi Liu ◽  
Junpeng Zhang ◽  
Heng Yin ◽  
...  
Keyword(s):  
Cleft Palate ◽  
Automatic Detection ◽  
Cleft Palate Speech ◽  
Glottal Stop

Automatic detection of consonant omission in cleft palate speech

2018 ◽  
Vol 22 (1) ◽  
pp. 59-65
Author(s):  
Ling He ◽  
Xiyue Wang ◽  
Jing Zhang ◽  
Qi Liu ◽  
Heng Yin ◽  
...  
Keyword(s):  
Cleft Palate ◽  
Automatic Detection ◽  
Cleft Palate Speech

Automatic hypernasality grade assessment in cleft palate speech based on the spectral envelope method

2020 ◽  
Vol 65 (1) ◽  
pp. 73-86 ◽  
Author(s):  
Jing Zhang ◽  
Sen Yang ◽  
Xiyue Wang ◽  
Ming Tang ◽  
Heng Yin ◽  
...  
Keyword(s):  
Cleft Palate ◽  
Speech Intelligibility ◽  
Linear Prediction ◽  
Vocal Tract ◽  
Experimental Results ◽  
Spectral Envelope ◽  
Support Vector ◽  
Cleft Palate Speech ◽  
Hypernasal Speech ◽  
Normal Speech

AbstractDue to velopharyngeal incompetence, airflow overflows from the oral cavity to the nasal cavity, which results in hypernasality. Hypernasality greatly reduces speech intelligibility and affects the daily communication of patients with cleft palate. Accurate assessment of hypernasality grades can provide assisted diagnosis for speech-language pathologists (SLPs) in clinical settings. Utilizing a support vector machine (SVM), this paper classifies speech recordings into four grades (normal, mild, moderate and severe hypernasality) based on vocal tract characteristics. Linear prediction (LP) analysis is widely used to model the vocal tract. Glottal source information may be included in the LP-based spectrum. The stabilized weighted linear prediction (SWLP) method, which imposes the temporal weights on the closed-phase interval of the glottal cycle, is a more robust approach for modeling the vocal tract. The extended weighted linear prediction (XLP) method weights each lagged speech signal separately, which achieves a finer time scale on the spectral envelope than the SWLP method. Tested speech recordings were collected from 60 subjects with cleft palate and 20 control subjects, and included a total of 4640 Mandarin syllables. The experimental results showed that the spectral envelope of normal speech decreases faster than that of hypernasal speech in the high-frequency part. The experimental results also indicate that the SWLP- and XLP-based methods have smaller correlation coefficients between normal and hypernasal speech than the LP method. Thus, the SWLP and XLP methods have better ability to distinguish hypernasal from normal speech than the LP method. The classification accuracies of the four hypernasality grades using the SWLP and XLP methods range from 83.86% to 97.47%. The selection of the model order and the size of the weight function are also discussed in this paper.

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