Enhanced Speech Recognition for Indonesian Geographic Dictionary Using Deep Learning

Hugeng, Hugeng and Gunawan, D and Kusumo, A. T. Enhanced Speech Recognition for Indonesian Geographic Dictionary Using Deep Learning. Enhanced Speech Recognition for Indonesian Geographic Dictionary Using Deep Learning.

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Abstract

Speech recognition technology has been developing
very fast lately. One of its application is to know the meaning of
some terms included in a geographic dictionary. When a subject
speaks a word to the system, it will output the word and its
meaning and explanation. There are many methods that are
applied to speech recognition. One of the methods that can be
applied and improve the accuracy of speech recognition is the
use of a deep learning method, i.e. Convolutional Neural
Network (CNN). In this research, CNN's speech recognition
accuracy for the Indonesian geographic dictionary is analyzed to
show that CNN can improve the accuracy of speech recognition
compared to speech recognition with Gaussian mixture model
and hidden Markov model (GMM-HMM). CNN is one of deep
learning methods that analyzes and finds similarity in
Mel-frequency cepstral coefficients (MFCC) from sound waves.
This research is performed by making models of the spoken
words using CNN under Python and TensorFlow. CNN is trained
with these models from speech data collected and prepared from
20 students, consists of 19 men and a woman of different ages
from 19 to 23 years. The vocabulary of the database consists of
50 words. The result of this research is a desktop application with
the trained models implemented. Our application can recognize
well the spoken words from subjects. Testing of the trained
models was performed to examine the accuracy of the build
speech recognition system. The result of the CNN speech
recognition method from the Indonesian geographic dictionary
is 80% accuracy for isolated words and 72.67% for continuous
words in our research.

Item Type:	Article
Subjects:	Penelitian > Fakultas Teknik
Divisions:	Fakultas Teknik > Teknik Elektro
Depositing User:	Puskom untar untar
Date Deposited:	19 Dec 2020 08:32
Last Modified:	19 Dec 2020 08:32
URI:	https://repotest.untar.ac.id/id/eprint/13693

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