Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/13796
Title: Design and implementation of an efficient speaker independent speech recognition system
Researcher: Uma Maheswari N.
Guide(s): Kapilan, A.P.
Keywords: Independent speech recognition system, Indian English, American English, British English Speeches, Efficient Speaker, Hidden Markov Model, Probabilistic Neural Network, Recurrent Neural Network
Upload Date: 9-Dec-2013
University: Anna University
Completed Date: 
Abstract: While speaker dependent speech recognition systems have achieved close to 90% accuracy, the speaker independent speech recognition systems have poorer efficiency. Speech recognition systems used in real time applications involve complex algorithms for faithful recognition. In this thesis, we describe a Speaker Independent Speech Recognition System for Indian English, American English and British English speeches. The entire procedure is divided into four stages: the initial stage deals with the general processing of the speech input, the second stage deals with preprocessing of the input speech and learning of the sound units. The third stage performs phoneme recognition using two-level neural networks, Probabilistic Neural Network and Recurrent Neural Network. The fourth stage executes word recognition and text recognition from the string of phonemes employing Hidden Markov Model. The system is trained by Indian English speech consisting of 300 words uttered by 60 speakers. The Speaker Independent Speech Recognition system was tested for 10 Indian English speakers live and showed a recognition rate of 76.8%, the higher error rate due to ambient noise. The Speaker Independent Speech Recognition system was also tested for isolated digits from 0 to 9 uttered by 3 speakers live and achieved a recognition rate of 98.3%. Then, the Speaker Independent Speech Recognition System is trained by American English speech consisting of 250 words uttered by 50 speakers. The test samples comprised 250 words spoken by a different set of 30 speakers. The recognition accuracy is found to be 89.1% on an average which is better than the previous results. Further, the Speaker Independent newlineSpeech Recognition System is trained by British English speech consisting of 200 words uttered by 30 speakers. The test samples comprised 200 words spoken by a different set of 20 speakers. The recognition accuracy is found to be 92.3% on an average which is well above the previous results. newline newline newline
Pagination: xix, 115
URI: http://hdl.handle.net/10603/13796
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File49.63 kBAdobe PDFView/Open
02_certificates.pdf1 MBAdobe PDFView/Open
03_abstract.pdf13.12 kBAdobe PDFView/Open
04_acknowledgement.pdf13.31 kBAdobe PDFView/Open
05_contents.pdf46.51 kBAdobe PDFView/Open
06_chapter 1.pdf59.44 kBAdobe PDFView/Open
07_chapter 2.pdf61.99 kBAdobe PDFView/Open
08_chapter 3.pdf247.7 kBAdobe PDFView/Open
09_chapter 4.pdf1.86 MBAdobe PDFView/Open
10_chapter 5.pdf13.87 kBAdobe PDFView/Open
11_references.pdf43.52 kBAdobe PDFView/Open
12_publications.pdf16.66 kBAdobe PDFView/Open
13_vitae.pdf10.49 kBAdobe PDFView/Open


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