Publication: An attention Long Short-Term Memory based system for automatic classification of speech intelligibility
dc.affiliation.dpto | UC3M. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Procesado Multimedia | es |
dc.contributor.author | Fernandez Diaz, Miguel | |
dc.contributor.author | Gallardo Antolín, Ascensión | |
dc.contributor.funder | Ministerio de Ciencia e Innovación (España) | es |
dc.date.accessioned | 2021-09-06T11:50:28Z | |
dc.date.available | 2022-11-01T00:00:06Z | |
dc.date.issued | 2020-11 | |
dc.description.abstract | Speech 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.sponsorship | The 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.extent | 8 | |
dc.identifier.bibliographicCitation | Fernández-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.doi | https://doi.org/10.1016/j.engappai.2020.103976 | |
dc.identifier.issn | 0952-1976 | |
dc.identifier.publicationfirstpage | 1 | |
dc.identifier.publicationissue | 103976 | |
dc.identifier.publicationlastpage | 8 | |
dc.identifier.publicationtitle | Engineering Applications of Artificial Intelligence | en |
dc.identifier.publicationvolume | 96 | |
dc.identifier.uri | https://hdl.handle.net/10016/33236 | |
dc.identifier.uxxi | AR/0000025407 | |
dc.language.iso | eng | |
dc.publisher | Elsevier | en |
dc.relation.projectID | Gobierno de España. TEC2017-84395-P | es |
dc.rights | © 2020 Elsevier Ltd. | en |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.rights.accessRights | open access | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject.eciencia | Telecomunicaciones | es |
dc.subject.other | Speech intelligibility | en |
dc.subject.other | Dysarthria | en |
dc.subject.other | Long Short-Term Memory (LSTM) | en |
dc.subject.other | Attention model | en |
dc.subject.other | Machine learning | en |
dc.title | An attention Long Short-Term Memory based system for automatic classification of speech intelligibility | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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