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Published in Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing (ROCLING 2020), 2020
Received the Best Paper Nomination ROCLING 2020
Recommended citation: Li-chia Chang and Jeih-weih Hung. 2020. The preliminary study of robust speech feature extraction based on maximizing the accuracy of states in deep acoustic models. In Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing (ROCLING 2020), pages 118–119, Taipei, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
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Published in Appl. Syst. Innov. Volume 5, Issue 4, 2022
This study proposes a novel robust speech feature extraction technique to improve speech recognition performance in noisy environments. This novel method exploits the information provided by the original acoustic model in the automatic speech recognition (ASR) system to learn a deep neural network that converts the original speech features.
Recommended citation: Chang, L.-C.; Hung, J.-W. A Preliminary Study of Robust Speech Feature Extraction Based on Maximizing the Probability of States in Deep Acoustic Models. Appl. Syst. Innov. 2022, 5, 71.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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