Akademik Veri Yönetim Sistemi
2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Türkiye, 11 - 13 Haziran 2021, ss.1-5
Voice recognition systems mostly suffer from environmental effects and accent differences. Therefore, studies
on speech recognition have begun to be examined using deep
learning which is a method known to be successful in speech
recognition and classification. In this study, 12 different voice
commands are defined using convolutional neural network, which
is a deep learning structure. In this study, the effect of dataset size
on test and recognition accuracy was investigated. In addition,
a different dataset which was prepared from the records of
people whose main language is Turkish to investigate the effect
of different accents on both test and recognition accuracy. In
the experiments when the test dataset including native-speaker
voice records is used, the test accuracy was obtained as 94.64%
for large dataset and 64.81% for small dataset. On the other
hand when the test dataset including foreigner’s voice records
the test accuracy reduced to 63.29% for large and 33.18% for small data set.