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pro vyhledávání: '"Shih, Yi-Jen"'
Autor:
Shih, Yi-Jen, Harwath, David
Self-supervised speech (SSL) models have recently become widely adopted for many downstream speech processing tasks. The general usage pattern is to employ SSL models as feature extractors, and then train a downstream prediction head to solve a speci
Externí odkaz:
http://arxiv.org/abs/2406.12209
Autor:
Wang, Hsuan-Fu, Shih, Yi-Jen, Chang, Heng-Jui, Berry, Layne, Peng, Puyuan, Lee, Hung-yi, Wang, Hsin-Min, Harwath, David
The recently proposed visually grounded speech model SpeechCLIP is an innovative framework that bridges speech and text through images via CLIP without relying on text transcription. On this basis, this paper introduces two extensions to SpeechCLIP.
Externí odkaz:
http://arxiv.org/abs/2402.06959
Autor:
Fang, Hung-Chieh, Ye, Nai-Xuan, Shih, Yi-Jen, Peng, Puyuan, Wang, Hsuan-Fu, Berry, Layne, Lee, Hung-yi, Harwath, David
Recent advances in self-supervised speech models have shown significant improvement in many downstream tasks. However, these models predominantly centered on frame-level training objectives, which can fall short in spoken language understanding tasks
Externí odkaz:
http://arxiv.org/abs/2402.05819
Autor:
Tseng, Yuan, Berry, Layne, Chen, Yi-Ting, Chiu, I-Hsiang, Lin, Hsuan-Hao, Liu, Max, Peng, Puyuan, Shih, Yi-Jen, Wang, Hung-Yu, Wu, Haibin, Huang, Po-Yao, Lai, Chun-Mao, Li, Shang-Wen, Harwath, David, Tsao, Yu, Watanabe, Shinji, Mohamed, Abdelrahman, Feng, Chi-Luen, Lee, Hung-yi
Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information. However, current models often focus on a limited set of tasks, and generalization abilities of l
Externí odkaz:
http://arxiv.org/abs/2309.10787
This work investigates the use of large-scale, English-only pre-trained models (CLIP and HuBERT) for multilingual image-speech retrieval. For non-English image-speech retrieval, we outperform the current state-of-the-art performance by a wide margin
Externí odkaz:
http://arxiv.org/abs/2211.01180
Data-driven speech processing models usually perform well with a large amount of text supervision, but collecting transcribed speech data is costly. Therefore, we propose SpeechCLIP, a novel framework bridging speech and text through images to enhanc
Externí odkaz:
http://arxiv.org/abs/2210.00705
Attention-based Transformer models have been increasingly employed for automatic music generation. To condition the generation process of such a model with a user-specified sequence, a popular approach is to take that conditioning sequence as a primi
Externí odkaz:
http://arxiv.org/abs/2111.04093
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