Curvelet Based Speech Recognition System in Noisy Environment : A Statistical Approach

Abstract

Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker's variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non-stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.

Implementation of Efficient Speech Recognition System on Mobile Device for Hindi and English Language

“…In Curvelet based technique Automatic Speech recognition in the noisy environment along with input speech signals disintegrated at various other frequencies, channels, applying available descriptions of Curvelet conversion to reduce estimated obstacles and size of feature vectors. Also, it has more accuracy, fluctuating size of a window because they observed much suitable for immobile signals [5]. The distinct Hidden Markov model can be used for better speech recognition and classification also it considers as time distribution of word signal.…”