title: GFM-based methods for speaker identification.
abstract: This paper presents three novel methods for speaker identification of which two methods utilize both the continuous density hidden Markov model (HMM) and the generalized fuzzy model (GFM), which has the advantages of both Mamdani and Takagi-Sugeno models. In the first method, the HMM is utilized for the extraction of shape-based batch feature vector that is fitted with the GFM to identify the speaker. On the other hand, the second method makes use of the Gaussian mixture model (GMM) and the GFM for the identification of speakers. Finally, the third method has been inspired by the way humans cash in on the mutual acquaintances while identifying a speaker. To see the validity of the proposed models [HMM-GFM, GMM-GFM, and HMM-GFM (fusion)] in a real-life scenario, they are tested on VoxForge speech corpus and on the subset of the 2003 National Institute of Standards and Technology evaluation data set. These models are also evaluated on the corrupted VoxForge speech corpus by mixing with different types of noisy signals at different values of signal-to-noise ratios, and their performance is found superior to that of the well-known models.
authors.affiliation: Netaji Subhas Institute of Technology, University of Delhi, New Delhi 110 078, India.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Bhardwaj
authors.forename: Smriti
authors.initials: S
authors.lastname: Srivastava
authors.forename: Madasu
authors.initials: M
authors.lastname: Hanmandlu
authors.forename: J R P
authors.initials: JR
authors.lastname: Gupta
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D017143:Fuzzy Logic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008390:Markov Chains; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D013068:Speech Production Measurement / Q000379:methods*