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dc.creatorCoto Jiménez, Marvin
dc.creatorGoddard Close, John
dc.creatorMartínez Licona, Fabiola
dc.date.accessioned2022-03-28T19:45:54Z
dc.date.available2022-03-28T19:45:54Z
dc.date.issued2016
dc.identifier.citationhttps://link.springer.com/chapter/10.1007/978-3-319-43958-7_42es_ES
dc.identifier.isbn978-3-319-43958-7
dc.identifier.urihttps://hdl.handle.net/10669/86306
dc.descriptionPart of the Lecture Notes in Computer Science book series (LNCS, volume 9811).es_ES
dc.description.abstractAutomatic speech recognition systems (ASR) suffer from performance degradation under noisy conditions. Recent work, using deep neural networks to denoise spectral input features for robust ASR, have proved to be successful. In particular, Long Short-Term Memory (LSTM) autoencoders have outperformed other state of the art denoising systems when applied to the mfcc’s of a speech signal. In this paper we also consider denoising LSTM autoencoders (DLSTMA), but instead use three different DLSTMAs and apply each to the mfcc’s, fundamental frequency, and energy features, respectively. Results are given using several kinds of additive noise at different intensity levels, and show how this collection of DLSTMA’s improves the performance of the ASR in comparison with the LSTM autoencoder.es_ES
dc.description.sponsorshipUniversidad de Costa Rica/[]/UCR/Costa Ricaes_ES
dc.description.sponsorshipConsejo Nacional de Ciencia y Tecnología/[CB-2012-01, No.182432]/CONACyT/Méxicoes_ES
dc.language.isoenges_ES
dc.sourceSpeech and Computer (pp.354-361).Budapest, Hungría: Springer, Chames_ES
dc.subjectLong short-term memory (LSTM)es_ES
dc.subjectDeep learninges_ES
dc.subjectDenoising autoencoderses_ES
dc.titleImproving automatic speech recognition containing additive noise using deep denoising autoencoders of lstm networkses_ES
dc.typecomunicación de congresoes_ES
dc.identifier.doi10.1007/978-3-319-43958-7_42
dc.description.procedenceUCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería Eléctricaes_ES


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