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Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/17645
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dc.contributor.authorCutajar, Michelle-
dc.contributor.authorGatt, Edward-
dc.contributor.authorGrech, Ivan-
dc.contributor.authorCasha, Owen-
dc.contributor.authorMicallef, Joseph-
dc.date.accessioned2017-03-20T10:53:29Z-
dc.date.available2017-03-20T10:53:29Z-
dc.date.issued2013-
dc.identifier.citationCutajar, M., Gatt, E., Grech, I., Casha, O., & Micallef, J. (2013). Comparative study of automatic speech recognition techniques. IET Signal Processing, 7(1), 25-46.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar//handle/123456789/17645-
dc.descriptionThe research work disclosed in this publication is partially funded by the Strategic Educational Pathways Scholarship Scheme (Malta). The scholarship is part-financed by the European Union – European Social Fund.en_GB
dc.description.abstractOver the past decades, extensive research has been carried out on various possible implementations of automatic speech recognition (ASR) systems. The most renowned algorithms in the field of ASR are the mel-frequency cepstral coefficients and the hidden Markov models. However, there are also other methods, such as wavelet-based transforms, artificial neural networks and support vector machines, which are becoming more popular. This review article presents a comparative study on different approaches that were proposed for the task of ASR, and which are widely used nowadays.en_GB
dc.language.isoenen_GB
dc.publisherInstitution of Engineering and Technologyen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectAutomatic speech recognitionen_GB
dc.subjectHidden Markov modelsen_GB
dc.subjectRadial basis functionsen_GB
dc.subjectSupport vector machinesen_GB
dc.titleComparative study of automatic speech recognition techniquesen_GB
dc.typearticleen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.1049/iet-spr.2012.0151-
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