Zobrazeno 1 - 10
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pro vyhledávání: '"Shi,Xuan"'
Speech2rtMRI: Speech-Guided Diffusion Model for Real-time MRI Video of the Vocal Tract during Speech
Understanding speech production both visually and kinematically can inform second language learning system designs, as well as the creation of speaking characters in video games and animations. In this work, we introduce a data-driven method to visua
Externí odkaz:
http://arxiv.org/abs/2409.15525
Accurate automatic speech recognition (ASR) for children is crucial for effective real-time child-AI interaction, especially in educational applications. However, off-the-shelf ASR models primarily pre-trained on adult data tend to generalize poorly
Externí odkaz:
http://arxiv.org/abs/2409.13095
Autor:
Cheng, Austin, Ser, Cher Tian, Skreta, Marta, Guzmán-Cordero, Andrés, Thiede, Luca, Burger, Andreas, Aldossary, Abdulrahman, Leong, Shi Xuan, Pablo-García, Sergio, Strieth-Kalthoff, Felix, Aspuru-Guzik, Alán
Publikováno v:
Faraday Discuss., 2024
Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspecti
Externí odkaz:
http://arxiv.org/abs/2409.10304
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behavior, and sensory processing. One important research area in ASD is evaluating children's behavioral changes over tim
Externí odkaz:
http://arxiv.org/abs/2409.09340
Autor:
Lee, Jihwan, Kommineni, Aditya, Feng, Tiantian, Avramidis, Kleanthis, Shi, Xuan, Kadiri, Sudarsana, Narayanan, Shrikanth
Speech decoding from EEG signals is a challenging task, where brain activity is modeled to estimate salient characteristics of acoustic stimuli. We propose FESDE, a novel framework for Fully-End-to-end Speech Decoding from EEG signals. Our approach a
Externí odkaz:
http://arxiv.org/abs/2406.08644
Automatic Speech Understanding (ASU) aims at human-like speech interpretation, providing nuanced intent, emotion, sentiment, and content understanding from speech and language (text) content conveyed in speech. Typically, training a robust ASU model
Externí odkaz:
http://arxiv.org/abs/2404.17983
Automatic Speech Understanding (ASU) leverages the power of deep learning models for accurate interpretation of human speech, leading to a wide range of speech applications that enrich the human experience. However, training a robust ASU model requir
Externí odkaz:
http://arxiv.org/abs/2306.07791
Publikováno v:
Frontiers in Medicine, Vol 11 (2024)
BackgroundOptimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective treatment plans typically require the comprehensive integration of cell morphology, immunology, cytogenetics, and molecul
Externí odkaz:
https://doaj.org/article/f61b837e523f438abb6864e70e68c7da
Autor:
Shi, Xuan1 (AUTHOR), Feng, Tiantian1 (AUTHOR), Huang, Kevin1 (AUTHOR), Kadiri, Sudarsana Reddy1 (AUTHOR), Lee, Jihwan1 (AUTHOR), Lu, Yijing2 (AUTHOR) tiantiaf@usc.edu, Zhang, Yubin2 (AUTHOR), Goldstein, Louis2 (AUTHOR), Narayanan, Shrikanth1,2 (AUTHOR)
Publikováno v:
JASA Express Letters. Nov2024, Vol. 4 Issue 11, p1-7. 7p.
Autor:
Feng, Tiantian, Hebbar, Rajat, Mehlman, Nicholas, Shi, Xuan, Kommineni, Aditya, Narayanan, and Shrikanth
Publikováno v:
APSIPA Transactions on Signal and Information Processing, vol. 12, no. 3, 2023
Speech-centric machine learning systems have revolutionized many leading domains ranging from transportation and healthcare to education and defense, profoundly changing how people live, work, and interact with each other. However, recent studies hav
Externí odkaz:
http://arxiv.org/abs/2212.09006