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Hindi Automatic Speech Recognition Using HTK

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Author(s): Preeti Saini1 , Parneet Kaur2 , Mohit Dua

Journal: International Journal of Engineering Trends and Technology
ISSN 2231-5381

Volume: 4;
Issue: 6;
Start page: 2223;
Date: 2013;
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Keywords: HMM | HTK | Mel Frequency Cepstral Coefficient (MFCC) | Automatic Speech Recognition (ASR) | Hindi | Isolated word ASR.

ABSTRACT
Automated Speech Recognition (ASR) is the ability of a machine or program to recognize the voice commands or take dictation which involves the ability to match a voice pattern against a provided or acquired vocabulary. At present, mainly Hidden Markov Model (HMMs) based speech recognizers are used. This paper aims to build a speech recognition system for Hindi language. Hidden Markov Model Toolkit (HTK) is used to develop the system. It recognizes the isolated words using acoustic word model. The system is trained for 113 Hindi words. Training data has been collected from nine speakers. The experimental results show that the overall accuracy of the presented system with 10 states in HMM topology is 96.61 and 95.49%.

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