Abstract:
Automatic Speech Recognition (ASR)
system is to accurately and efficiently convert
speech signal into a text message independent of
device, speaker or the environment. Feature
extraction is the second component of automatic
speech recognition systems which extract the
information from the speech frame. The feature
extraction is needed because the raw speech
signal contains information besides the Linguistic
message and has a high dimensionality. The
primary objective of feature extraction is to find
robust and discriminative features in the acoustic
data. The recognition module uses the speech
features and the acoustic models to decode the
speech input and produces text results with high
accuracy. There are several techniques for feature
extraction , this paper is the comparative analysis
of four feature extraction techniques of Filter
Bank (FBank), Mel Frequency Cepstral
Coefficient (MFCC), Perceptual Linear Predictive
(PLP) and Gammatone Frequency Cepstral
Coeffcieint (GFCC) for Myanmar continuous ASR.
The experimental result shows that with the
classification method Gaussian Mixture Model
(GMM). The better performance of feature
extraction method is to support for Myanmar ASR.
