Pathology Voice Detection and Classification Using Ensemble Learning
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 8
Abstract
Voice disorder is a large phenomenon which is dramatically affecting a large number of people. These pathological conditions are caused due to various reasons and some of the reasons might be talking too much, screaming constantly, smoking which affects our vocal chords. Voice disorders can be treated properly when diagnosed early. The research work in this field generally splits in two stages: first, extraction of meaningful feature sets, and second, using these features for classification of speech recordings into healthy condition and different pathological cases. The objective is to use the discriminative features in the voice signals to detect the pathologically affected voices. Here Ensemble learning technique is used to find the types of disorder in voices. The first component is the extraction of feature vectors using Mel-frequency cepstral coefficients (MFCC), Linear predictive coding (LPC), Wavelet Packet Decomposition (WPD), Cepstral Analysis (CA), Jitter, Shimmer, Pulse, Pitch, Hormonicity, Intensity, Energy and entropy methods. The second is the classification of feature vectors using ensemble learning methods. The parameters from the voice signals are used to build the model. From the experimental results, it is observed that LibD3c classifier performs well in classifying the pathological voices.
Authors and Affiliations
Mythili J, Vijaya MS
Keywords
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