Diagnosis of Pathological Speech with Efficient and Effective Features for Long Short-Term Memory Learning

Abstract

The majority of voice disorders stem from improper vocal usage. Alterations in voice quality can also serve as indicators for a broad spectrum of diseases. Particularly, the significant correlation between voice disorders and dental health underscores the need for precise diagnosis through acoustic data. This paper introduces effective and efficient features for deep learning with speech signals to distinguish between two groups: individuals with healthy voices and those with pathological voice conditions. Using a public voice database, the ten-fold test results obtained from long short-term memory networks trained on the combination of time-frequency and time-space features with a data balance strategy achieved the following metrics: accuracy = 90%, sensitivity = 93%, specificity = 87%, precision = 88%,F1score = 0.90, and area under the receiver operating characteristic curve = 0.96.