Speech Query Recognition for Tamil Language Using Wavelet and Wavelet Packets

Abstract

Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

Efficient Approaches to Computer Vision and Pattern Recognition

“…Iswarya and Radha [11] proposed a new approach to speech query recognition using wavelet packet; they called it combined thresholding for noise removal. It concentrates on improving the mel frequency cepstral coefficients (MFCC) features by introducing discrete wavelet packet transform (DWPT) in place of the discrete Fourier transformation (DFT) block to provide efficient signal analysis.…”