Abstract
Speaker Identification(SI) has numerous applications in real world. Traditional classifiers like Gaussian Mixture Models (GMM), Support Vector Machine (SVM), and Hidden Markov Models (HMM) were used earlier for SI. Features like Mel Frequency Cepstral Coefficient (MFCC), and Gammatone Frequency Cepstral Coefficients (GFCC) need to be generated first. But these approaches do not perform well when audio data captured through multiple devices and recorded in different environments, i.e., in mismatch condition. Whereas Machine Learning (ML) algorithms usually provide better accuracy, and hence became more popular. Restricted Boltzmann Machine(RBM), Long-Short-Term Memory (LSTM), and Convolutional neural network (CNN) are some of the ML approaches applied on SI. In this paper, CNN is used for automatic feature extraction and speaker classification on IITG-MV noisy dataset. CNN performs better than GMM, specially for device mismatch case.
Sign-in/Register to access full text options 
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.
