Combining LLM-generated text, speaker-cloned TTS speech, and a dynamic importance loss improves automated speaking assessment accuracy by 3.3% on seen prompts and under 1% on unseen prompts versus real-data-only training on the LTTC dataset.
Please generate your response in the style of the above examples
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions
Combining LLM-generated text, speaker-cloned TTS speech, and a dynamic importance loss improves automated speaking assessment accuracy by 3.3% on seen prompts and under 1% on unseen prompts versus real-data-only training on the LTTC dataset.