Project

Speech Encoding in Impaired Hearing

Acronym
ROBSPEAR
Code
41T00116
Duration
01 October 2016 → 31 March 2022
Funding
European funding: framework programme
Principal investigator
Research disciplines
  • Engineering and technology
    • Other (bio)medical engineering not elsewhere classified
Keywords
impaired hearing machine learning hearing diagnostics auditory modelling auditory evoked potentials speech intelligibility
Other information
 
Project description

The prevalence of hearing impairment amongst the elderly is a stunning 33%, while the younger generation is sensitive to noise-induced hearing loss through increasingly loud urban life and lifestyle. Yet, hearing impairment is inadequately diagnosed and treated because we fail to understand how the components that constitute a hearing loss impact robust speech encoding. A recent and ground-breaking discovery in animal physiology demonstrated the existence of a noise-induced hearing deficit -cochlear neuropathy- that coexists with the well-studied cochlear gain loss deficit known to degrade the audibility of sound. Cochlear neuropathy is thought to impact robust encoding of the audible portions of speech and occurs before standard hearing screening methods indicate problems, implying that a large group of noiseexposed people with self-reported hearing problems is currently not screened, nor treated. To design effective hearing restoration strategies, it is crucial to understand how cochlear neuropathy interacts with other hearing deficits to affect robust speech encoding in every-day listening conditions. Through an interdisciplinary approach, RobSpear targets hearing deficits along the ascending stages of the auditory pathway to revolutionize how hearing impairment is diagnosed and treated. RobSpear can yield immense reductions of health care costs through effective treatment of currently misdiagnosed patients and studies the impact of noiseinduced hearing deficits on our society. To achieve this, RobSpear: (i) Builds a hearing profile that, based on a computational model of the auditory periphery, develops physiological measures that differentially diagnose hearing deficits in listeners with mixtures of deficits. (ii) Designs individually tailored speech enhancement algorithms that work in adverse conditions and target perceptually relevant speech features, using an unprecedented validation approach that combines novel psychoacoustic and physiological metrics.

 
 
 
Disclaimer
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency (ERCEA). Neither the European Union nor the authority can be held responsible for them.