| Paper ID | SPE-47.6 |
| Paper Title |
REFINING AUTOMATIC SPEECH RECOGNITION SYSTEM FOR OLDER ADULTS |
| Authors |
Liu Chen, Meysam Asgari, Oregon Health and Science University, United States |
| Session | SPE-47: Speech Recognition 17: Speech Adaptation and Normalization |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-RECO] Acoustic Modeling for Automatic Speech Recognition |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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 the attention mechanism to utilize the model’s intermediate information. Utilizing our intuitive conditional-independent attention mechanism, our optimal model achieves $1.58\%$ absolute improvements over the TL model. |
