  An efficient speech to text converter for mobile application is presented in
this work. The prime motive is to formulate a system which would give optimum
performance in terms of complexity, accuracy, delay and memory requirements for
mobile environment. The speech to text converter consists of two stages namely
front-end analysis and pattern recognition. The front end analysis involves
preprocessing and feature extraction. The traditional voice activity detection
algorithms which track only energy cannot successfully identify potential
speech from input because the unwanted part of the speech also has some energy
and appears to be speech. In the proposed system, VAD that calculates energy of
high frequency part separately as zero crossing rate to differentiate noise
from speech is used. Mel Frequency Cepstral Coefficient (MFCC) is used as
feature extraction method and Generalized Regression Neural Network is used as
recognizer. MFCC provides low word error rate and better feature extraction.
Neural Network improves the accuracy. Thus a small database containing all
possible syllable pronunciation of the user is sufficient to give recognition
accuracy closer to 100%. Thus the proposed technique entertains realization of
real time speaker independent applications like mobile phones, PDAs etc.
