Syllable based word identification for malayalam speech using machine learning (Record no. 4910)

MARC details
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fixed length control field 02783nam a22001937a 4500
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control field 20220107122808.0
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fixed length control field 160226b xxu||||| |||| 00| 0 eng d
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Transcribing agency
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Personal name Maya Moneykumar (93514008)
9 (RLIN) 8206
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Title Syllable based word identification for malayalam speech using machine learning
502 ## - DISSERTATION NOTE
Degree type Master of Philosophy in Computer Science
Name of granting institution 2014-2015
Year degree granted INT
-- Elizabeth Sherly
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Summary, etc. This thesis aims at discussing the development of an isolated word identification system<br/>for Malayalam using Machine Learning techniques. This work examines how Artificial<br/>Neural Network (ANN) and Hidden Markov Model (HMM) can benefit a medium size vocabulary, speaker independent isolated word level recognition system. The goal of this work is to design an ANN based word recognition system and evaluate its accuracy in different modes namely words within the vocabulary as well as out of vocabulary. The<br/>recognition accuracy is also tested for speaker dependent as well speaker independent<br/>modes. The system was then compared with a similar system performance based on<br/>HMM. The work aims at syllable based word identification where each and every utterance will be segmented into corresponding syllables which are in turn trained by the system. Currently, most speech recognition systems are based on Hidden Markov Model (HMM) which is a statistical framework that supports both acoustic and temporal modeling. In this work, the system is trained using syllables segmented from the utterances where a new approach is made to do the syllable segmentation efficiently, based on the energy measure, formant frequencies and zero crossing rate. These segmented syllables are then trained using HMM and ANN to compare the recognition accuracy. To compare the two systems, we have kept similar, the train and test data and also the extracted features. The comparison includes the overall system performance and different test accuracy rates for both the models. The system is trained using multiple utterances of 80 different words by 9 different speakers, 6 male and 3 female, where the feature extraction was done using MFCC, the most powerful feature extraction technique. In this work, the speech recognition engine is built using HTK and WEKA. The work also attempts to evaluate the improvement in recognition accuracy of the system based on ANN by training and testing with additional parameters. The system proved successful in identifying the utterances of out of vocabulary words, which indeed is a notable step in the area of speech recognition.<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTING METHODOLOGIES
9 (RLIN) 8207
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Topical term or geographic name entry element ARTIFICIAL INTELLIGENCE
9 (RLIN) 8208
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Topical term or geographic name entry element NATURAL LANGUAGE PROCESSING
9 (RLIN) 8209
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Topical term or geographic name entry element SPEECH RECOGNITION
9 (RLIN) 8210
942 ## - ADDED ENTRY ELEMENTS (KOHA)
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    Dewey Decimal Classification     IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre 26/02/2016   R-627 26/02/2016 26/02/2016 Project Reports