5 papers
Reference-aware SFM layers for intrusive intelligibility prediction
Hanlin Yu, Haoshuai Zhou, Boxuan Cao +3
Intrusive speech-intelligibility predictors that exploit explicit reference signals are now widespread, yet they have not consistently surpassed non-intrusive systems. We argue tha…
Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners
Boxuan Cao, Linkai Li, Hanlin Yu +3
Speech intelligibility evaluation for hearing-impaired (HI) listeners is essential for assessing hearing aid performance, traditionally relying on listening tests or intrusive meth…
A Multi-stage Low-latency Enhancement System for Hearing Aids
Chengwei Ouyang, Kexin Fei, Haoshuai Zhou +2
This paper proposes an end-to-end system for the ICASSP 2023 Clarity Challenge. In this work, we introduce four major novelties: (1) a novel multi-stage system in both the magnitud…
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…
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 (…