Abstract
Voice analysis serves as a crucial tool in diagnosing voice abnormalities, offering a non-invasive alternative to intrusive procedures. This research digs into a comprehensive study of voice disorder assessment methodologies, focusing on acoustic analysis and classification. The study employs various parameters such as Jitter, Shimmer, and Harmonic-to-Noise Ratio (HNR) alongside an Artificial Neural Network (ANN) classifier. Utilizing the Saarbruecken Voice Database the research aims to distinguish between healthy and dysphonic voices across genders. Principal Component Analysis (PCA) aids in feature selection, enhancing model accuracy. The results exhibit distinct precision levels in male and female groups, showcasing the effectiveness of specific parameters in classification.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
