3 citations · 3 across the 3 of their papers we have counts for
3 papers
eess.AS2025
No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction
Haoshuai Zhou, Changgeng Mo, Boxuan Cao +2
Personalized speech intelligibility prediction is challenging. Previous approaches have mainly relied on audiograms, which are inherently limited in accuracy as they only capture a…
cs.AI2025
Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People
Haoshuai Zhou, Boxuan Cao, Changgeng Mo +2
Speech foundation models (SFMs) have demonstrated strong performance across a variety of downstream tasks, including speech intelligibility prediction for hearing-impaired people (…
cs.CV2021★ 3 cited
Interpreting Audiograms with Multi-stage Neural Networks
Shufan Li, Congxi Lu, Linkai Li +3
Audiograms are a particular type of line charts representing individuals' hearing level at various frequencies. They are used by audiologists to diagnose hearing loss, and further…