TY  - GEN
N2  - Automatic speech recognition (ASR) is an essential component for building automatic cognitive assessment systems designed to monitor older adults' cognitive status. While, in the ASR field, remarkable achievements have been reported on publicly available academic datasets, two under-explored problems are important to building automatic cognitive assessment systems: ASRs' performance on aging voice and accuracy in transcribing keywords. Both problems are important to deliver high-quality transcriptions for assessment purposes. In this dissertation, we focus on developing transfer learning techniques/methods to build ASRs that perform well on older adults with possible cognitive impairment. Firstly, we present a transfer learning technique to improve an open-source ASR's performance on older adults (80+ years old) with limited data (i.e., about 10 hours of audio recordings). We demonstrate that the aging voice dramatically impacts an ASR's performance and that adapting the ASR with older adults' recording data through fine-tuning can improve the performance. We propose a transfer learning technique that utilizes intermediate outputs to increase the fine-tuning efficiency with limited training data. This technique achieves better performance than the standard fine-tuning.
DO  - 10.6083/bpxhc42382
DO  - doi
AB  - Automatic speech recognition (ASR) is an essential component for building automatic cognitive assessment systems designed to monitor older adults' cognitive status. While, in the ASR field, remarkable achievements have been reported on publicly available academic datasets, two under-explored problems are important to building automatic cognitive assessment systems: ASRs' performance on aging voice and accuracy in transcribing keywords. Both problems are important to deliver high-quality transcriptions for assessment purposes. In this dissertation, we focus on developing transfer learning techniques/methods to build ASRs that perform well on older adults with possible cognitive impairment. Firstly, we present a transfer learning technique to improve an open-source ASR's performance on older adults (80+ years old) with limited data (i.e., about 10 hours of audio recordings). We demonstrate that the aging voice dramatically impacts an ASR's performance and that adapting the ASR with older adults' recording data through fine-tuning can improve the performance. We propose a transfer learning technique that utilizes intermediate outputs to increase the fine-tuning efficiency with limited training data. This technique achieves better performance than the standard fine-tuning.
AD  - University of Oregon Health Sciences Center
T1  - Automatic speech recognition for small data and its application on cognitive assessment
DA  - 2023-12-15
AU  - Chen, Liu
L1  - https://digitalcollections.ohsu.edu/record/42382/files/Chen.Liu.2023.pdf
PB  - Oregon Health and Science University
LA  - eng
PY  - 2023-12-15
ID  - 42382
L4  - https://digitalcollections.ohsu.edu/record/42382/files/Chen.Liu.2023.pdf
KW  - Aged
KW  - Cognitive Dysfunction
KW  - small data
KW  - automatic speech recognition
TI  - Automatic speech recognition for small data and its application on cognitive assessment
Y1  - 2023-12-15
L2  - https://digitalcollections.ohsu.edu/record/42382/files/Chen.Liu.2023.pdf
LK  - https://digitalcollections.ohsu.edu/record/42382/files/Chen.Liu.2023.pdf
UR  - https://digitalcollections.ohsu.edu/record/42382/files/Chen.Liu.2023.pdf
ER  -