An Acoustic-Signal-Based Preventive Program for University Lecturers' Vocal Health

Professional activities of university lecturers involve continued and sustained use of the voice, leading in many cases to increased risk of developing voice disorders. Risk identification followed by the fast application of preventive or corrective measures is a key issue in this context.

Define and implement a preventive program for the vocal health of university lecturers by using acoustic features automatically extracted from voice recordings to identify risk groups and manage preventive or corrective actions MATERIAL AND METHODS: A total of 170 subjects, aged between 18 and 65, were recruited at the San Pedro de Alcántara Hospital and at the University of Extremadura in Cáceres (Spain). They formed three groups-one of 25 people suffering from vocal fold nodules, another of 25 healthy people, and the third of 120 university lecturers. Medical history and voice status assessment was performed, and voice recordings were made following a research protocol. A feature extraction, selection, and classification procedure was applied to the voice recordings to provide the best predictors for discriminating between pathological and healthy voices. The model parameters were then used to determine the lecturers' probability of suffering vocal fold nodules or other pathologies with similar dysphonic speech. These probabilities were used to classify the lecturers into three risk groups-low, medium, and high. These groups were taken as the basis to assign the lecturers to a primary, secondary, or tertiary prevention level. Different preventive or corrective actions were applied for each prevention level.

The best set of predictors comprised sample entropy, correlation dimension, pitch period entropy, glottal noise excitation, and sex, achieving an overall accuracy of 92% with a random forest classifier. They all showed statistically significant differences between vocal fold nodules and healthy groups (P < 0.05). Three out of the four best acoustic features were nonlinear, showing the importance of nonlinear dynamics for clinical practice. The model parameters were applied to the predictors of the lecturers so as to assign them to the different risk groups, leading to 60.8% (73 out of 120) of the lecturers in the low-risk group, 29.2% (35 out of 120) in the medium-risk group, and 10% (12 out of 120) in the high-risk group. The prevention levels were assigned on the basis of this classification and the medical history and laryngological evaluation of some specific subjects. A statistically significant association was found between the voice status and the assigned prevention level (P < 0.001), with there being a clear dependence relationship (Cramér's V = 0.630).

It is feasible to develop and apply a preventive voice program for university lecturers that is aided by features automatically extracted from voice recordings. As the program progresses, it is expected that the information automatically provided for the assignment to prevention levels will become ever more precise. The method proposed can be extended to other voice professionals and other voice disorders.