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DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture.

, , , , , , and . Trans. Mach. Learn. Res., (2022)

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DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture., , , , , , and . Trans. Mach. Learn. Res., (2022)On the Use of Pitch Features for Disordered Speech Recognition., , , and . INTERSPEECH, page 4130-4134. ISCA, (2019)LF-MMI Training of Bayesian and Gaussian Process Time Delay Neural Networks for Speech Recognition., , , , , , , and . INTERSPEECH, page 2793-2797. ISCA, (2019)Gaussian Process Neural Networks for Speech Recognition., , , , , , , and . INTERSPEECH, page 1778-1782. ISCA, (2018)Generalizing Few-Shot NAS with Gradient Matching., , , , , and . ICLR, OpenReview.net, (2022)Exploiting Prompt Learning with Pre-Trained Language Models for Alzheimer's Disease Detection., , , , , , and . ICASSP, page 1-5. IEEE, (2023)Audio-Visual Multi-Channel Integration and Recognition of Overlapped Speech., , , , , , , , and . IEEE ACM Trans. Audio Speech Lang. Process., (2021)DSNAS: Direct Neural Architecture Search Without Parameter Retraining., , , , , , and . CVPR, page 12081-12089. Computer Vision Foundation / IEEE, (2020)Development of the Cuhk Elderly Speech Recognition System for Neurocognitive Disorder Detection Using the Dementiabank Corpus., , , , , , , , , and 1 other author(s). ICASSP, page 6433-6437. IEEE, (2021)Mixed Precision Quantization of Transformer Language Models for Speech Recognition., , , , and . ICASSP, page 7383-7387. IEEE, (2021)