Master of Science (MS)
First Committee Member
Number of Pages
This research mainly focuses on recognizing the speakers through their speech samples. Numerous "Text-Dependent" or "Text-Independent" algorithms have been developed by people so far, to recognize the speaker from his/her speech. In this thesis, we concentrate on the recognition of the speaker from the fixed text i.e. "Text-Dependent". Possibility of extending this method to variable text i.e. "Text-Independent" is also analyzed. Different feature extraction algorithms are employed and their performance with Artificial Neural Networks as a Data Classifier on a fixed training set is analyzed. We find a way to combine all these individual feature extraction algorithms by incorporating their interdependence. The efficiency of these algorithms is determined after the input speech is classified using Back Propagation Algorithm of Artificial Neural Networks. A special case of Back Propagation Algorithm which improves the efficiency of the classification is also discussed.
Artificial; Identification; Networks; Neural; Robust; Speaker
Computer science; Electrical engineering
University of Nevada, Las Vegas
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Sivathanu Pillai, Madhavan, "Robust speaker identification using artificial neural networks" (2006). UNLV Retrospective Theses & Dissertations. 2068.
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