Abstract
A speaker's accent is the most important factor affecting the performance of automatic speech recognition (ASR) systems. This is due to the fact that accents vary widely, even within the same country or community. The reason may be attributed to the fuzziness between the boundaries of phoneme classes, a result of differences in a speaker's vocal tract and accent. In this paper, a new method of accent classification is proposed that is based on fuzzy Gaussian mixture models (FGMMs). The proposed method first uses a fuzzy clustering to fuzzily partition the data. In this way, fuzzy memberships to the cluster centres are determined by minimizing the distance between the cluster centres and feature vectors. Afterwards, a GMM classifier is trained by using the fuzzy Gaussian parameters to classify the speaker's accent. The experimental results show that the proposed method outperforms the Gaussian Mixture models, Vector Quantization modeling method, Hidden Markov Model, and Radial Basis Neural Networks.
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.