학술논문

Using Machine Learning to Understand the Relationships Between Audiometric Data, Speech Perception, Temporal Processing, And Cognition
Document Type
Conference
Source
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Auditory system
Machine learning
Aging
Signal processing
Electrophysiology
Cognition
Reverberation
Machine Learning
Audiology
Speech Perception
Temporal Processing
Language
ISSN
2379-190X
Abstract
Aging and hearing loss cause communication difficulties, particularly for speech perception in demanding situations, which have been associated with factors including cognitive processing and extended high-frequency (>8 kHz) hearing. Quantifying such associations and finding other (possibly unintuitive) associations is well suited to machine learning. We constructed ensemble models for 443 participants who varied in age and hearing loss. Audiometric, perceptual, electrophysiological, and cognitive data were used to predict speech perception in noise, reverberation, and with time compression. Speech perception was best predicted by variables associated with audiometric thresholds (including new across-frequency composite variables) between 1–4 kHz, followed by basic temporal processing ability. Cognitive factors and extended high-frequency thresholds had little to no predictive ability of speech perception. Future associations or lack thereof will inform the field as we attempt to better understand the intertwined effects of speech perception, aging, hearing loss, and cognition.