Combined Sustained Vowels Improve the Performance of the Haar Wavelet for Pathological Voice Characterization

This paper investigates the feasibility of combining sustained vowels for computer-based pathological voice characterization. Although recent work has shown that single sustained vowels enable accurate voice characterization, the literature lacks evidence about combining them. Here we propose a pipeline for abnormal voice characterization and classification that encompasses a feature extraction method based on the Haar wavelet. We also evaluate the benefits of our pipeline when using an input signal that comprises three different sustained vowels instead of single ones. We conducted experiments on samples of sustained vowels /a/, /i/, and /u/ from two public datasets, exploring the wavelet decomposition levels in the range of 4 to 18. Our results revealed that wavelet coefficients extracted from the combination of vowels improved signal description and, hence, identification of subtle features of pathological voices. We also showed that the Haar wavelet-based features extracted from combined vowels achieved accurate voice classification with fewer decomposition levels. This approach enabled accuracy improvements of at least 15.61 and 2.61% for SVD and AVFAD datasets, respectively, regardless of the biological gender.