Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.
Collaboration network of the authors
2 authors ·
1 co-authorship link
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
Fiji National University
Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.