ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2021
REFINING AUTOMATIC SPEECH RECOGNITION SYSTEM FOR OLDER ADULTS.
Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Detecting cognitive impairment and psychological well-being among older adults.Machine learning. Health · 2026Article
- Changes in conversational pattern as a clinical trial outcome: a proof-of-concept study using the I-CONECT data.Innovation in aging · 2026Article
- Can speech foundation models effectively identify languages in low-resource multilingual aging populations?JASA express letters · 2025Article
- Continual learning framework for a multicenter study with an application to electrocardiogram.BMC medical informatics and decision making · 2024Article
- The Joint Effects of Acoustic and Linguistic Markers for Early Identification of Mild Cognitive Impairment.Frontiers in digital health · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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 an attention mechanism to utilize the model's intermediate information. Our approach achieves 1.58% absolute improvements over the TL model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.