Papers with Speech-to-text
Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)
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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang
| Challenge: | Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets. |
| Approach: | They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains. |
| Outcome: | The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings. |
Indic-TEDST: Datasets and Baselines for Low-Resource Speech to Text Translation (2024.lrec-main)
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| Challenge: | Speech-to-text Translation (ST) tasks are performed by human translators with proficiency in both the source and target languages. |
| Approach: | a new study compares the performance of SOTA ST models on low-resource languages . the authors propose to use a dataset to compare the models on high-resourced languages based on the results of their research . |
| Outcome: | a new study shows that only a few models have performed well on low-resource languages . the results indicate the need for specialized models for low- and high-resourced languages based on the dataset . |