Open AccessPerformance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit
Author(s) -
Daniele Ravì
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1047.0782s219
Subject(s) - mel frequency cepstrum , computer science , speech recognition , hidden markov model , word error rate , artificial intelligence , classifier (uml) , word (group theory) , pattern recognition (psychology) , feature (linguistics) , word recognition , feature extraction , natural language processing , linguistics , philosophy , reading (process) , political science , law
A digit recognition system is built for recognizing the sequence of digits through 0-9. The system is experimented with speech corpus created in the room environment. The acoustic information to feature representation is achieved using PLP and MFCC features. The system initially utilized the conventional GMM-HMM framework, state of the art hybrid classifier with varied number of states to complete the speech recognition task, i.e., the system is first trained and tested using Monophone models, and system’s recognition accuracy is then evaluated using Triphone Models: Triphone1 models, which was later followed by Triphones2 models and Triphones3 Models. The Ngram Language model is used for both Monophone and Triphone training. The system performance is evaluated with the use of MFCC and PLP parameterisation techniques on Kaldi toolkit. The system performance is evaluated using metrics word error rate (WER) and Word Recognition Accuracy (WRA). The proposed system can be utilized for building speech applications