Speech Signal Analysis and Classification of Dominant Parameter for Pathological Voices
Christina Subiksha W1, Nandhini A2, Bharath K P3, Mahalti Mohammed Sohail4, Rajesh Kumar M5
1Christina Subiksha W, School of Electronics Engineering, VIT University, Vellore, India.
2Nandhini A, School of Electronics Engineering, VIT University, Vellore, India.
3Bharath K P, School of Electronics Engineering, VIT University, Vellore, India.
4Mahalti Mohammed Sohail, School of Electronics Engineering, VIT University, Vellore, India.
5Rajesh Kumar M*, School of Electronics Engineering, VIT University, Vellore, India.
Manuscript received on May 06, 2020. | Revised Manuscript received on May 17, 2020. | Manuscript published on June 10, 2020. | PP: 225-231 | Volume-9 Issue-8, June 2020. | Retrieval Number: 100.1/ijitee.H6312069820 | DOI: 10.35940/ijitee.H6312.069820
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The primary objective of the project is to analyze speech signals by determining the important parameters that affect the voice of an individual which leads to various voice disorders. The analysis is carried out based on the individual’s age and gender with the help of the pattern recognized from each sample and the value of each parameter is compared with the nominal values of the healthy person with respect to their age and gender using the Praat software. The secondary objective is the classification of the voice signal into normal and abnormal voice samples using the machine learning software Konstanz Information Miner (KNIME).
Keywords: Harmonics-to-noise ratio (HNR), Jitter, Konstanz Information Miner (KNIME), Praat, Shimmer.
Scope of the Article: Classification