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Researchers enhance elderly ASR with LLM paraphrasing and speech synthesis

By PulseAugur Editorial · [1 sources] ·

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

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

RANK_REASON Academic paper detailing a new method for data augmentation in ASR.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Researchers enhance elderly ASR with LLM paraphrasing and speech synthesis

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Academic paper detailing a new method for data augmentation in ASR.
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Minsik Lee, Seoi Hong, Chongmin Lee, Sieun Choi, Jian Kim, Jua Han, Jihie Kim ·

    Elderly-Contextual Data Augmentation via Speech Synthesis for Elderly ASR

    arXiv:2604.24770v1 Announce Type: new Abstract: Despite recent progress in automatic speech recognition (ASR), elderly ASR (EASR) remains challenging due to limited training data and the distinct acoustic and linguistic characteristics of elderly speech. In this work, we address …

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