Pathological voice classification system based on CNN-BiLSTM network using speech enhancement and multi-stream approach

The paper developing a resilient speech classification system for individuals with voice disorders poses a formidable challenge due to the significant variability and distortions inherent in vocal signals. This article outlines the steps to create an effective classification system for pathological speech. The first step involved applying speech enhancement processing using the minimum mean square error (MMSE) enhancer to improve voice input data quality and intelligibility. Secondly, a multi-stream approach combined various acoustic vectors based on human auditory perception, including mel-spectrogram images, mel frequency cepstral coefficients (MFCC), power normalized cepstral coefficients (PNCC), and prosodic parameters like F0, Jitter, and Shimmer. Finally, a deep machine learning incorporating both a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network was employed to process these enhanced characteristics in a multi-stream framework, resulting in a powerful classification system architecture. In our experiments, we utilized two subsets from the Massachusetts Eye and Ear Infirmary (MEEI) database, each involving distinct causes of voice disorders. The first subset consisted of voice recordings from patients with vocal nodules, paralysis, and polyps, while the second subset included recordings from patients with mild ventricular compression, A–P squeezing, and gastric reflux. The results we obtained reveal that the CNN-BiLSTM system, coupled with a robust speech analysis interface based on the multi-stream approach and enhanced by the minimum mean square error (MMSE) processing, achieved the highest accuracy rates.