000			02783nam a22001937a 4500
003			OSt
005			20220107122808.0
008			160226b xxu||||| |||| 00| 0 eng d
040			_c
100			_aMaya Moneykumar (93514008) _98206
245			_aSyllable based word identification for malayalam speech using machine learning
502			_bMaster of Philosophy in Computer Science _c2014-2015 _dINT _eElizabeth Sherly
520			_aThis thesis aims at discussing the development of an isolated word identification system for Malayalam using Machine Learning techniques. This work examines how Artificial 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 recognition accuracy is also tested for speaker dependent as well speaker independent modes. The system was then compared with a similar system performance based on 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.
650			_aCOMPUTING METHODOLOGIES _98207
650			_aARTIFICIAL INTELLIGENCE _98208
650			_aNATURAL LANGUAGE PROCESSING _98209
650			_aSPEECH RECOGNITION _98210
942			_2ddc _cPR
999			_c4910 _d4910