Researchers enhance elderly ASR with LLM paraphrasing and speech synthesis

Researchers have developed a novel data augmentation technique to improve automatic speech recognition (ASR) for elderly individuals. This method utilizes large language models to paraphrase existing transcripts, generating elderly-contextual variations. These paraphrased texts are then converted into synthetic speech using text-to-speech synthesis with elderly reference speakers. Experiments demonstrated a significant reduction in word error rate, with up to a 58.2% improvement compared to baseline models. AI

影响 Enhances ASR performance for specific demographics, potentially improving accessibility of voice technologies for the elderly.

排序理由 Academic paper detailing a new method for data augmentation in ASR.

在 arXiv cs.CL 阅读 →

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