Exploring speech characteristics for automatic pathological voice detection
M. Włoszczyńska, Bożena Kostek · The Journal of the Acoustical Society of America · 2023
This paper aims to explore speech characteristics for automatic pathological voice detection. First, the assumptions underlying the conducted experiments are presented, along with examples of databases containing pathological speech. Then, a selection process of features of the speech signal and algorithms used to distinguish between undisturbed and pathological speech are discussed. Two deep models are employed to perform binary classification of the speech signal. Their structure is presented along with the feature space chosen. The results of classifying undisturbed and pathological speech are compared with state-of-the-art literature sources. The accuracy values are comparable to those presented in the literature, i.e., they are within the range of 61%–71% for our research and 64%–98% for the literature data, depending on the algorithms and databases used. An application is also built to illustrate the speech recognition process. The experiments are summarized with conclusions, and the direction of possible future development of the research performed is given.