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dc.contributor.authorNisar, S
dc.contributor.authorTariq, M
dc.contributor.authorAdeel, A
dc.contributor.authorGogate, M
dc.contributor.authorHussain, A
dc.date.accessioned2020-01-10T12:54:10Z
dc.date.available2020-01-10T12:54:10Z
dc.date.issued2019-02-13
dc.identifier.citationNisar, S., Tariq, M., Adeel, A. et al. (2019) Cognitively inspired feature extraction and speech recognition for automated hearing loss testing, Cognitive Computation, 11, pp. 489–502. doi:10.1007/s12559-018-9607-4en
dc.identifier.issn1866-9956en
dc.identifier.doi10.1007/s12559-018-9607-4en
dc.identifier.urihttp://hdl.handle.net/2436/622980
dc.description.abstract© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Hearing loss, a partial or total inability to hear, is one of the most commonly reported disabilities. A hearing test can be carried out by an audiologist to assess a patient’s auditory system. However, the procedure requires an appointment, which can result in delays and practitioner fees. In addition, there are often challenges associated with the unavailability of equipment and qualified practitioners, particularly in remote areas. This paper presents a novel idea that automatically identifies any hearing impairment based on a cognitively inspired feature extraction and speech recognition approach. The proposed system uses an adaptive filter bank with weighted Mel-frequency cepstral coefficients for feature extraction. The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment. Comparative performance evaluation demonstrates the potential of our automated hearing test method to achieve comparable results to the clinical ground truth, established by the expert audiologist’s tests. The overall absolute error of the proposed model when compared with the expert audiologist test is less than 4.9 dB and 4.4 dB for the pure tone and speech audiometry tests, respectively. The overall accuracy achieved is 96.67% with a hidden Markov model (HMM). The proposed method potentially offers a second opinion to audiologists, and serves as a cost-effective pre-screening test to predict hearing loss at an early stage. In future work, authors intend to explore the application of advanced deep learning and optimization approaches to further enhance the performance of the automated testing prototype considering imperfect datasets with real-world background noise.en
dc.formatapplication/pdfen
dc.languageen
dc.language.isoenen
dc.publisherSpringer Science and Business Media LLCen
dc.relation.urlhttps://link.springer.com/article/10.1007%2Fs12559-018-9607-4en
dc.subjectHearing lossen
dc.subjectSpeech recognitionen
dc.subjectmachine learningen
dc.subjectAutomationen
dc.subjectCognitive radioen
dc.titleCognitively inspired feature extraction and speech recognition for automated hearing loss testingen
dc.typeJournal articleen
dc.identifier.eissn1866-9964
dc.identifier.journalCognitive Computationen
dc.date.updated2020-01-08T18:12:34Z
dc.date.accepted2018-10-23
rioxxterms.funderEPSRC/ deepCI.org.en
rioxxterms.identifier.projectUOW10012020AAen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2020-02-13en
dc.source.volume11
dc.source.issue4
dc.source.beginpage489
dc.source.endpage502
dc.description.versionPublished version
refterms.dateFCD2020-01-10T12:53:36Z
refterms.versionFCDAM


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