Gavrilova, Marina LOhi, Abu Quwsar2024-08-202024-08-202024-08-19Ohi, A. Q. (2024). Open-set speaker recognition with bounded Laguerre Voronoi clustering (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.https://hdl.handle.net/1880/119443Speaker recognition is a challenging problem in behavioral biometrics. It has been rigorously investigated over the last decade. Although numerous supervised closed-set systems successfully harvest the power of deep neural networks, limited studies have been made on open-set speaker recognition. This thesis proposes a self-supervised open-set speaker recognition that leverages the geometric properties of speaker distribution for accurate and robust speaker identification. The proposed framework consists of a deep neural network incorporating a wider viewpoint of temporal speech features and Laguerre–Voronoi diagram-based speech feature extraction. The deep neural network is trained with a specialized clustering criterion that only requires positive pairs during training. The framework further incorporates a novel approach of clustering by integrating concepts from Voronoi diagrams in Laguerre geometry. This approach offers flexibility by necessitating only one hyperparameter, an upper-bound value for the number of centroids. The experiments validated that the proposed system outperformed current state-of-the-art methods in open-set speaker verification and identification.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Computer ScienceArtificial Intelligencemaster thesis