Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/17645
Title: Comparative study of automatic speech recognition techniques
Authors: Cutajar, Michelle
Gatt, Edward
Grech, Ivan
Casha, Owen
Micallef, Joseph
Keywords: Automatic speech recognition
Hidden Markov models
Radial basis functions
Support vector machines
Issue Date: 2013-02
Publisher: Institution of Engineering and Technology
Citation: Cutajar, M., Gatt, E., Grech, I., Casha, O., & Micallef, J. (2013). Comparative study of automatic speech recognition techniques. IET Signal Processing, 7(1), 25-46.
Abstract: Over 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.
Description: The 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.
URI: https://www.um.edu.mt/library/oar//handle/123456789/17645
Appears in Collections:Scholarly Works - FacICTMN

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