NDLI: Developing efficient speech recognition system for Telugu letter recognition

Content Provider	IEEE Xplore Digital Library
Author	Venkateswarlu, R.L.K. Teja, R.R. Kumari, R.V.
Copyright Year	2012
Description	Author affiliation: Sasi Institute of Technology and Engineering, Tadepalligudem (Venkateswarlu, R.L.K.) \|\| Government College, Puducherry, India (Kumari, R.V.) \|\| R. L. K Institute of Mathematics, Rajahmundry (Teja, R.R.)
Abstract	Telugu is the third largest language spoken by nearly 80 million native speakers. Telugu is one of four classical languages in India. Telugu is the official language for the state of Andhra Pradesh. Each telugu word ends with vowels. So there is a scope for research about Telugu vowels recognition rate. The application of machine learning techniques to biometric authentication and recognition problems has gained a widespread acceptance. In this research, both MLP and TLRN models were trained and tested on a dataset that consists of Four different speakers (2Male and 2Female) are allowed to utter the letters for 10 times. Recognition of the telugu letters is carried out in speaker dependent mode. In this mode the tested data presented to the network are same as the trained data. A comparative study of the application of Multilayer Perceptron (MLP) and Time Lagged Recurrent Neural Network (TLRN) in speech recognition has been carried out with the features LPCC and MFCC to obtained spectral and statistical parameters. The goal of speech recognition in biometrics is to verify an individual's identity based on his or her utterance. It is found that the proposed system outperforms the conventional system with both the features LPCC and MFCC. Promising results are obtained both in the training and testing phases due to exploration of discriminative information with neural networks. It is found that TLRN trains and tests faster than MLP. For both the convention system and proposed system, MFCC gives higher recognition accuracy in training and testing phases. The vowel recognition rate for the convention system with the features LPCC and MFCC are 92.47% and 94% respectively whereas for the proposed system it is 96% and 97.56% respectively.
Starting Page	1
Ending Page	6
File Size	999697
Page Count	6
File Format	PDF
ISBN	9781467302708
e-ISBN	9781467302739
DOI	10.1109/ICCCA.2012.6179184
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-02-22
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Time Lagged Recurrent Neural Network Filter banks Speech recognition Feature Extraction Speech Feature extraction Multilayer Perception Mel frequency cepstral coefficient Testing Telugu vowels
Content Type	Text
Resource Type	Article

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