Universiti Teknologi Malaysia Institutional Repository

Real-time voice-controlled game interaction using convolutional neural networks

Waqar, Dania Maryam and Gunawan, Teddy Surya and Kartiwi, Mira and Ahmad, Robiah (2021) Real-time voice-controlled game interaction using convolutional neural networks. In: 7th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2021, 23 - 25 August 2021, Virtual, Bandung.

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Official URL: http://dx.doi.org/10.1109/ICSIMA50015.2021.9526318


Speech recognition has gained growing popularity due to its wide applications in almost every field, ranging from wake-word recognition, emotion recognition, command recognition, and interactive game. Recently, there is a growing interest in using voice in the gaming industry. Voice-controlled interaction made gaming much more accessible to a wider audience. However, the use of voice to control games requires real-time processing to avoid unwanted delay. This paper proposes speech command recognition using Convolutional Neural Networks (CNN) to control the popular snake game. First, the limited dataset for Up, Down, Left, Right speech commands was prepared for training, validation, and testing. Second, an optimum MFCC and CNN-based speech command recognition were proposed to recognize the four speech command. Results showed that our proposed algorithm could achieve high recognition accuracy of 96.5% and was able to detect all four commands. Finally, the proposed algorithm is integrated with a Python-based snake game.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:CNN, interactive game, snake game, speech command recognition
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:96665
Deposited By: Widya Wahid
Deposited On:15 Aug 2022 16:23
Last Modified:15 Aug 2022 16:23

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