Abstract
Most of the existing modelling techniques for the speaker recognition task make an implicit assumption of sufficient data for speaker modelling and hence may lead to poor modelling under limited data condition. The present work gives an experimental evaluation of the modelling techniques like Crisp Vector Quantization (CVQ), Fuzzy Vector Quantization (FVQ), Self-Organizing Map (SOM), Learning Vector Quantization (LVQ), and Gaussian Mixture Model (GMM) classifiers. An experimental evaluation of the most widely used Gaussian Mixture Model-Universal Background Model (GMM-UBM) is also made. The experimental knowledge is then used to select a subset of classifiers for obtaining the combined classifiers. It is proposed that the combined LVQ and GMM-UBM classifier provides relatively better performance compared to all the individual as well as combined classifiers.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.