Refining Automatic Speech Recognition System for older adults
This addresses the challenge of developing effective ASR systems for a narrow, underserved population of older adults with potential cognitive impairments, representing an incremental improvement.
The paper tackled the problem of poor automatic speech recognition (ASR) performance for socially isolated seniors (80+ years old) with limited training data, achieving a 1.58% absolute improvement over a transfer learning baseline by incorporating an attention mechanism.
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.