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Detecting challenge from physiological signals: A primary study with a typical game scenario.

, , , , , and . CHI Extended Abstracts, page 424:1-424:7. ACM, (2022)

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Exploring Self-Supervised Pre-Trained ASR Models for Dysarthric and Elderly Speech Recognition., , , , , , , , and . ICASSP, page 1-5. IEEE, (2023)Bayesian Learning for Deep Neural Network Adaptation., , , and . CoRR, (2020)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)Efficient use of DNN bottleneck features in generalized variable parameter HMMs for noise robust speech recognition., , , and . INTERSPEECH, page 2474-2478. ISCA, (2015)Adversarial Data Augmentation for Disordered Speech Recognition., , , , , , and . Interspeech, page 4803-4807. ISCA, (2021)Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition., , , and . CoRR, (2022)Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition., , , , , , , and . IEEE ACM Trans. Audio Speech Lang. Process., (2022)Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition., , , , , , , and . CoRR, (2022)Investigation of Data Augmentation Techniques for Disordered Speech Recognition., , , , , , and . CoRR, (2022)