Discrimination between patients with CVDs and healthy people by voiceprint using the MFCC and Pitch

Abstract

Heart diseases cause many deaths around the world every year, and his death rate makes him the leader of the killer diseases. But early diagnosis can be helpful to decrease those several deaths and save lives. To ensure good diagnose, people must pass a series of clinical examinations and analyzes, which make the diagnostic operation expensive and not accessible for everyone. Speech analysis comes as a strong tool that can resolve the task and give back a new way to discriminate between healthy people and cardiovascular disease patients. Our latest paper treated this task but using a dysphonia measurement to differentiate between people with cardiovascular disease and the healthy one, and we were able to reach 81.5% in prediction accuracy. This time we choose to change the method to increase the accuracy by extracting the voiceprint using 13 Mel-Frequency Cepstral Coefficients and the pitch, extracted from the people's voices provided from 75 subjects (35 has cardiovascular diseases, 40 healthy), three records of sustained vowels (aaaaa…,ooooo…and iiiiiiii….) has been collected from each one. We used the k-near-neighbor classifier to train a model and to classify the test entities. We were able to outperform the previous results, reaching 95.55% of prediction accuracy.

Keywords

• voiceprint
• cardiovascular diseases
• speech analysis
• mfcc
• k-near-neighbor classifier