Open AccessAutomatic Speech Recognition (ASR) System for Isolated Marathi Words: Using HTK
Author(s) -
Sunil B. Patil,
Nita Patil,
Ajay S. Patil*
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l2651.1081219
Subject(s) - marathi , speech recognition , computer science , mel frequency cepstrum , hidden markov model , security token , viterbi algorithm , artificial intelligence , word (group theory) , mixture model , natural language processing , feature extraction , mathematics , philosophy , linguistics , geometry , computer security
The present manuscript focuses on building automatic speech recognition (ASR) system for Marathi language (M-ASR) using Hidden Markov Model Toolkit (HTK). The M-ASR system gives the detail about experimentation and implementation using the HTK Toolkit. In this work total 106 speaker independent Marathi isolated words were recognized. These unique Marathi words are used to train and evaluate M-ASR system. The speech corpus (database) is created by us using isolated Marathi words uttered with mixed gender people. The system uses Mel Frequency Cepstral Coefficient (MFCC) for the purpose of extracting features using Gaussian mixture model (GMM). Viterbi algorithm based on token passing is used for decoding to recognize unknown utterances. The proposed M-ASR system is speaker independent. The proposed system has reported 96.23% word level recognition accuracy.