Vocal-Feature Based Classification of Post-Laryngectomy Patients for Rehabilitation Monitoring

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Carullo, Alessio, Vallan, Alberto, Fantini, Marco, Succo, Giovanni
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; cepstral analysis; Classification; classification models; Entropy; Feature extraction; Harmonic analysis; Indexes; Intelligibility; Kurtosis; laryngectomy; objective evaluation; Regression models; Rehabilitation; Reliability; Resonant frequencies; Stability criteria; Statistical analysis; Surgery; Task analysis; Voice analysis; voice assessment
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3277947

This paper deals with the analysis of substitution voices in patients who underwent partial laryngectomy for laryngeal cancer, with the aim of identifying a reliable methodology to provide an objective evaluation of post-intervention phonatory impairment and of the effectiveness of rehabilitation therapies. The investigated data-set includes 85 patients who underwent type I Open Partial Horizontal Laryngectomy (22 subjects), type II OPHL (32 subjects) and type III OPHL (31 subjects). The available vocal material (reading task and sustained vowel) was pre-processed in order to remove non-harmonic frames from the patients' records using two different algorithms. After this preliminary step, a series of features that belong to time, spectral and cepstral domains were extracted from the selected harmonic frames. Then, two different comparisons were made between the classes OPHL-I vs OPHL-II+III and the classes OPHL-II+III(I< 5) vs OPHL-II+III(I≥ 5), where the index I (Intelligibility) of the auditory perceptual scale INFVo was assessed during a preliminary evaluation. Two different feature-selection techniques, which are based on the comparison among the probability distributions of the extracted features and the classification performance of a logistic regression model, identified the features with the best discrimination capabilities, which are harmonic-to-noise ratio, fundamental frequency, spectral kurtosis, spectral entropy and mel-frequency cepstral coefficients. The best classification accuracy of 96.5% (5-fold cross validation) was obtained in the comparison OPHL-I vs OPHL-II+III using a logistic regression model that was trained using the 5° and 95° percentile of the fundamental frequency and the 95° percentile of the spectral entropy extracted from the reading task.