# Elderly-Contextual Data Augmentation via Speech Synthesis for Elderly ASR

arXiv: https://arxiv.org/abs/2604.24770 · HTML: https://mlsanity.com/paper/2604.24770/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Builds on (most cited first, 17 of 17)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems](https://mlsanity.com/paper/1912.09257/) (2019)
- [OpenVoice: Versatile Instant Voice Cloning](https://mlsanity.com/paper/2312.01479/) (2023)
- [Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation](https://mlsanity.com/paper/2305.10951/) (2023)
- [Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2202.10290/) (2022)
- [Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2205.06445/) (2022)
- [Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2302.14564/) (2023)
- [Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesis](https://mlsanity.com/paper/2406.08568/) (2024)
- [Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions](https://mlsanity.com/paper/2407.17874/) (2024)
- [SeniorTalk: A Chinese Conversation Dataset with Rich Annotations for Super-Aged Seniors](https://mlsanity.com/paper/2503.16578/) (2025)
- [Frustratingly Easy Data Augmentation for Low-Resource ASR](https://mlsanity.com/paper/2509.15373/) (2025)
- [VOTE400(Voide Of The Elderly 400 Hours): A Speech Dataset to Study Voice Interface for Elderly-Care](https://mlsanity.com/paper/2101.11469/) (2021)
- [Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly Speaker Adaptation](https://mlsanity.com/paper/2407.06310/) (2024)
- [MOPSA: Mixture of Prompt-Experts Based Speaker Adaptation for Elderly Speech Recognition](https://mlsanity.com/paper/2505.24224/) (2025)
- [Large Language Model Data Generation for Enhanced Intent Recognition in German Speech](https://mlsanity.com/paper/2508.06277/) (2025)

## Abstract (arXiv)

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 data scarcity in EASR through a data augmentation pipeline that combines large language model (LLM)-based transcript paraphrasing with text-to-speech (TTS) synthesis. Given an elderly speech dataset, the LLM first generates elderly-contextual paraphrases of the original transcripts, and the TTS model then synthesizes corresponding speech using elderly reference speakers. The resulting synthetic audio-text pairs are merged with the original data to fine-tune Whisper without architectural modification. We further analyze the effects of augmentation ratio and reference-speaker composition in low-resource EASR. Experiments on English and Korean elderly speech datasets from speakers aged 70 and above show that the proposed method consistently improves performance over conventional augmentation baselines, achieving up to a 58.2% reduction in word error rate (WER) compared with the Whisper baseline.