# Refining Automatic Speech Recognition System for older adults

arXiv: https://arxiv.org/abs/2011.08346 · HTML: https://mlsanity.com/paper/2011.08346/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech](https://mlsanity.com/paper/2204.00145/) (2022)
- [Linguistic-Based Mild Cognitive Impairment Detection Using Informative Loss](https://mlsanity.com/paper/2402.01690/) (2024)
- [Feasibility of Detecting Cognitive Impairment and Psychological Well-being among Older Adults Using Facial, Acoustic, Linguistic, and Cardiovascular Patterns Derived from Remote Conversations](https://mlsanity.com/paper/2412.14194/) (2024)

## Abstract (arXiv)

Building a high quality automatic speech recognition (ASR) system with limited training data has been a challenging task particularly for a narrow target population. Open-sourced ASR systems, trained on sufficient data from adults, are susceptible on seniors' speech due to acoustic mismatch between adults and seniors. With 12 hours of training data, we attempt to develop an ASR system for socially isolated seniors (80+ years old) with possible cognitive impairments. We experimentally identify that ASR for the adult population performs poorly on our target population and transfer learning (TL) can boost the system's performance. Standing on the fundamental idea of TL, tuning model parameters, we further improve the system by leveraging an attention mechanism to utilize the model's intermediate information. Our approach achieves 1.58% absolute improvements over the TL model.