Abstract
Exoskeleton devices based on speech systems are often affected by noise in the actual working environment, which reduces the recognition rate of speech systems. In order to reduce the influence of noise on the speech system, this paper proposes using joint features of Mel Frequency Cepstrum Coefficients and Gammatone Frequency Cepstrum Coefficients. Then, an improved Fast Correlation-Based Filtering algorithm is used to perform dimensionality reduction and remove irrelevant and redundant features in the joint features to obtain the optimal feature subset. Finally, the experiments show that the feature recognition effect is greatly improved after dimensionality reduction is performed under a low signal-to-noise ratio.
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Acknowledgement
I would like to express my heartfelt thanks and sincere respect to Mr. Wenjie Chen. He has always encouraged me to continue to learn knowledge with his persistent pursuit of science, practical and realistic work attitude, generous and humble approach and selfless dedication. I also thank the National Natural Science Foundation of China (51975002) for its support.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Su, Z., Chen, W., Sun, X., Ding, N., Zhi, Y. (2023). A Feature Extraction Algorithm for Exoskeleton Speech Control System Based on Noisy Environment. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14268. Springer, Singapore. https://doi.org/10.1007/978-981-99-6486-4_32
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DOI: https://doi.org/10.1007/978-981-99-6486-4_32
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