Papers by Roshan Sharma
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions (2024.naacl-long)
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Siddhant Arora, Hayato Futami, Jee-weon Jung, Yifan Peng, Roshan Sharma, Yosuke Kashiwagi, Emiru Tsunoo, Karen Livescu, Shinji Watanabe
| Challenge: | Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model’s behavior and surpassing performance of task-specific models. |
| Approach: | They adapt a pre-trained automatic speech recognition model to additional tasks using single-token task specifiers. |
| Outcome: | The proposed model can generalize to new datasets and languages for seen task types. |
R-BASS : Relevance-aided Block-wise Adaptation for Speech Summarization (2024.findings-naacl)
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| Challenge: | End-to-end speech summarization on long recordings is challenging because of the high computational cost. |
| Approach: | They propose a new relevance-aware block-wise adaptation method that automatically estimates block relevance based on lexical and semantic similarity between transcript and summary. |
| Outcome: | The proposed method can drop 86.3 % of blocks while maintaining comparable performance. |
On the Evaluation of Speech Foundation Models for Spoken Language Understanding (2024.findings-acl)
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Siddhant Arora, Ankita Pasad, Chung-Ming Chien, Jionghao Han, Roshan Sharma, Jee-weon Jung, Hira Dhamyal, William Chen, Suwon Shon, Hung-yi Lee, Karen Livescu, Shinji Watanabe
| Challenge: | Spoken language understanding evaluation (SLUE) benchmarks are used to benchmark complex spoken language understanding tasks on natural speech. |
| Approach: | They propose a set of benchmark tasks to evaluate spoken language understanding on natural speech . they use pre-trained speech foundation models to evaluate the utility of different SFMs . |
| Outcome: | The proposed framework outperforms pre-trained speech foundation models on natural speech . the proposed framework also outperformed self-supervised SFMs on the sequence generation tasks . |