Comparative study of automatic speech recognition techniques
Michelle Cutajar, Edward Gatt, Ivan Grech, Owen Casha, Joseph Micallef · IET Signal Processing · 2013
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.