TY - GEN
T1 - Speech Recognition System Using DeepSpeech Architecture Method on VHF Radio Communication for Tanker Ship Officers at Indonesian Sea Ports
AU - Haq, Arina
AU - Suryanegara, Muhammad
N1 - Funding Information:
ACKNOWLEDGMENT The publication of this study was supported by Hibah Universitas Indonesia. This paper was part of Mrs. Arina’s bachelor project work, which has been supervised by Dr. Muhammad Suryanegara ([email protected]; [email protected]) as the corresponding author.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The loading and unloading of fuel oil cargo by tanker ships at ports in Indonesia has a problem in terms of time efficiency and speed. A virtual robotic is created to increase the time efficiency of the loading and unloading process. However, the robot needs a way to communicate with the tanker ship officers during the process. Because the port where the loading and unloading process took place is considered as a dangerous and explosive location, the only communication allowed is through voice communication via marine Very High Frequency (VHF) radio. The solution to overcome this problem is to design a technology that can perform speech recognition via marine VHF radio, one of which is using the Deep Learning method with DeepSpeech architecture. This paper has simulated speech recognition system using DeepSpeeh architecture method on VHF radio communication for tanker ship officers at sea ports. This paper has tested the DeepSpeech architecture to produce a speech recognition model with an average Word Error Rate (WER) value of 0.335 and an average Character Error Rate (CER) value of 0.263. This paper also analyzes the effect of variations in learning rate, dropout rate, and epoch value to get the best speech recognition system model.
AB - The loading and unloading of fuel oil cargo by tanker ships at ports in Indonesia has a problem in terms of time efficiency and speed. A virtual robotic is created to increase the time efficiency of the loading and unloading process. However, the robot needs a way to communicate with the tanker ship officers during the process. Because the port where the loading and unloading process took place is considered as a dangerous and explosive location, the only communication allowed is through voice communication via marine Very High Frequency (VHF) radio. The solution to overcome this problem is to design a technology that can perform speech recognition via marine VHF radio, one of which is using the Deep Learning method with DeepSpeech architecture. This paper has simulated speech recognition system using DeepSpeeh architecture method on VHF radio communication for tanker ship officers at sea ports. This paper has tested the DeepSpeech architecture to produce a speech recognition model with an average Word Error Rate (WER) value of 0.335 and an average Character Error Rate (CER) value of 0.263. This paper also analyzes the effect of variations in learning rate, dropout rate, and epoch value to get the best speech recognition system model.
KW - Deep Learning
KW - DeepSpeech
KW - Marine VHF radio
KW - Speech Recognition System
UR - http://www.scopus.com/inward/record.url?scp=85141881486&partnerID=8YFLogxK
U2 - 10.1109/ICITACEE55701.2022.9924031
DO - 10.1109/ICITACEE55701.2022.9924031
M3 - Conference contribution
AN - SCOPUS:85141881486
T3 - Proceedings - 2022 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
SP - 164
EP - 168
BT - Proceedings - 2022 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
A2 - Prakoso, Teguh
A2 - Riyadi, Munawar Agus
A2 - Arfan, M.
A2 - Soetrisno, Yosua Alvin Adi
A2 - Afrisal, Hadha
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
Y2 - 25 August 2022 through 26 August 2022
ER -