Cochlear implant technology has developed consistently and rapidly, since auditory prostheses first came into widespread use about twenty years ago. Nowadays, cochlear implant devices can guarantee that even profoundly deaf people can enjoy hearing sensation. Nonetheless, in conditions that include background noise even at low levels, cochlear implants have generally proven much less effective. In such settings implant users have great difficulty understanding speech. Bilateral cochlear implants seem capable of somewhat ameliorating this situation by considerably improving access to binaural auditory cues. Although current clinical data attest to substantial benefits over unilateral stimulation, many implanted users still experience difficulties while communicating in background noise. The proposed investigation aims to boost speech understanding in noisy scenarios, by using a novel speech processing strategy, known as blind signal separation (BSS). BSS is a statistical signal processing technique that can process multi-sensory observations of an inaccessible set of signals (sources) in a manner that reveals their individual (and original) form. More importantly, it can do so without assuming any prior knowledge regarding the mixing structure or the source signals themselves. BSS relies solely on the existence of two microphones and can therefore be applied to either a bilateral configuration (one microphone per ear) or a unilateral implant device (two microphones per ear). In both configurations, BSS can efficiently capitalize on the spatial cues being present in the mixtures of the signals received by the two microphones, and essentially use that information to spatially separate the target from the masker signals. The working hypothesis is that upon spatially segregating the target speech signal from the masker source by resorting to BSS, listeners can benefit from a substantial increase in speech recognition performance when compared to their daily strategy. The proposed study will focus on thoroughly assessing the potential of BSS as a commercially viable pre-processing technique in both bilateral and unilateral cochlear implant users. Our hypothesis will be tested within both anechoic and modest-to-severe reverberant settings. Word recognition tests will be conducted with (A) twenty (20) normal-hearing young adults using cochlear implant processing, (B) ten (10) postlingually deafened adults fitted binaurally with Nucleus 24 R implant devices using either the SPrint or ESPrit 3G sound processor and also (C) ten postlingually deafened adults fitted monaurally with a Nucleus 24 R device using the Freedom sound processor employing the BEAM strategy. Public Health Relevance: Cochlear implants can partially restore the advantages of normal hearing and guarantee that even profoundly deaf people can enjoy hearing sensation. Nonetheless, they are less effective when noise is present. This investigation aims to improve speech recognition in noisy settings, by using a novel speech processing strategy, ideally suited to both unilateral and bilateral cochlear implant users.

Public Health Relevance

Cochlear implants can partially restore the advantages of normal hearing and guarantee that even profoundly deaf people can enjoy hearing sensation. Nonetheless, they are less effective when noise is present. This investigation aims to improve speech recognition in noisy settings, by using a novel speech processing strategy, ideally suited to both unilateral and bilateral cochlear implant users.

Agency
National Institute of Health (NIH)
Institute
National Institute on Deafness and Other Communication Disorders (NIDCD)
Type
Small Research Grants (R03)
Project #
7R03DC008882-04
Application #
8434997
Study Section
Special Emphasis Panel (ZDC1-SRB-C (27))
Program Officer
Miller, Roger
Project Start
2009-06-02
Project End
2013-05-31
Budget Start
2011-08-02
Budget End
2013-05-31
Support Year
4
Fiscal Year
2011
Total Cost
$71,148
Indirect Cost
Name
University of Kansas Lawrence
Department
Other Health Professions
Type
Schools of Arts and Sciences
DUNS #
076248616
City
Lawrence
State
KS
Country
United States
Zip Code
66045
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