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An attention Long Short-Term Memory based system for automatic classification of speech intelligibility

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Procesado Multimediaes
dc.contributor.authorFernandez Diaz, Miguel
dc.contributor.authorGallardo Antolín, Ascensión
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2021-09-06T11:50:28Z
dc.date.available2022-11-01T00:00:06Z
dc.date.issued2020-11
dc.description.abstractSpeech intelligibility can be degraded due to multiple factors, such as noisy environments, technical difficulties or biological conditions. This work is focused on the development of an automatic non-intrusive system for predicting the speech intelligibility level in this latter case. The main contribution of our research on this topic is the use of Long Short-Term Memory (LSTM) networks with log-mel spectrograms as input features for this purpose. In addition, this LSTM-based system is further enhanced by the incorporation of a simple attention mechanism that is able to determine the more relevant frames to this task. The proposed models are evaluated with the UA-Speech database that contains dysarthric speech with different degrees of severity. Results show that the attention LSTM architecture outperforms both, a reference Support Vector Machine (SVM)-based system with hand-crafted features and a LSTM-based system with Mean-Pooling.en
dc.description.sponsorshipThe work leading to these results has been partly supported by the Spanish Government-MinECo under Project TEC2017-84395-P. The authors wish to acknowledge Dr. Mark Hasegawa-Johnson for making the UA-Speech database available.en
dc.format.extent8
dc.identifier.bibliographicCitationFernández-Díaz, M. & Gallardo-Antolín, A. (2020). An attention Long Short-Term Memory based system for automatic classification of speech intelligibility. Engineering Applications of Artificial Intelligence, 96, 103976.en
dc.identifier.doihttps://doi.org/10.1016/j.engappai.2020.103976
dc.identifier.issn0952-1976
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue103976
dc.identifier.publicationlastpage8
dc.identifier.publicationtitleEngineering Applications of Artificial Intelligenceen
dc.identifier.publicationvolume96
dc.identifier.urihttps://hdl.handle.net/10016/33236
dc.identifier.uxxiAR/0000025407
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. TEC2017-84395-Pes
dc.rights© 2020 Elsevier Ltd.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherSpeech intelligibilityen
dc.subject.otherDysarthriaen
dc.subject.otherLong Short-Term Memory (LSTM)en
dc.subject.otherAttention modelen
dc.subject.otherMachine learningen
dc.titleAn attention Long Short-Term Memory based system for automatic classification of speech intelligibilityen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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