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Dataset for Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners

Dataset for Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners
Dataset for Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners
Dataset supporting 'Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners' published in the Journal of the Acoustical Society of America. Dataset showing the raw (anonymized) data of intelligibility, quality and audiograms. These data allow complete reconstruction of figures in paper Dataset DOI assigned 10.5258/SOTON/D0020
Speech intelligibility, Artificial neural networks, Signal processing, Speech recognition, Testing procedures
University of Southampton
Bleeck, Stefan
c888ccba-e64c-47bf-b8fa-a687e87ec16c
MONAGHAN, JESSICA
c6e0821f-a660-4f07-85ac-66033f0e0b44
Bleeck, Stefan
c888ccba-e64c-47bf-b8fa-a687e87ec16c
MONAGHAN, JESSICA
c6e0821f-a660-4f07-85ac-66033f0e0b44

Bleeck, Stefan and MONAGHAN, JESSICA (2017) Dataset for Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners. University of Southampton doi:10.5258/SOTON/D0020 [Dataset]

Record type: Dataset

Abstract

Dataset supporting 'Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners' published in the Journal of the Acoustical Society of America. Dataset showing the raw (anonymized) data of intelligibility, quality and audiograms. These data allow complete reconstruction of figures in paper Dataset DOI assigned 10.5258/SOTON/D0020

Spreadsheet
data.xlsx - Dataset
Available under License Creative Commons Attribution.
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More information

Published date: 6 March 2017
Related URLs:
Keywords: Speech intelligibility, Artificial neural networks, Signal processing, Speech recognition, Testing procedures
Organisations: Human Sciences Group

Identifiers

Local EPrints ID: 406458
URI: http://eprints.soton.ac.uk/id/eprint/406458
DOI: doi:10.5258/SOTON/D0020
PURE UUID: 6978095f-464a-45f5-97b3-529971f35e5b
ORCID for Stefan Bleeck: ORCID iD orcid.org/0000-0003-4378-3394

Catalogue record

Date deposited: 10 Mar 2017 22:52
Last modified: 04 Nov 2023 02:42

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Contributors

Creator: Stefan Bleeck ORCID iD
Creator: JESSICA MONAGHAN

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