Open AccessComparative study of automatic speech recognition techniques
Author(s) -
Cutajar Michelle,
Gatt Edward,
Grech Ivan,
Casha Owen,
Micallef Joseph
Publication year - 2013
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2012.0151
Subject(s) - computer science , mel frequency cepstrum , speech recognition , hidden markov model , task (project management) , artificial intelligence , field (mathematics) , artificial neural network , cepstrum , implementation , pattern recognition (psychology) , speech processing , feature extraction , mathematics , pure mathematics , programming language , management , economics
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.