Speech Recognition of Isolated Malayalam Words Using Wavelet Features and Artificial Neural Network
V. R. Vimal Krishnan, Athulya Jayakumar, Anto P. Babu · 2008
This paper deals a novel speech feature extraction technique based on wavelet. We have employed daubechies 4 type of wavelet for feature extraction. Artificial neural network technique (ANN) is used for classification and recognition purpose. We have used five Malayalam (one of the South Indian languages) words for the experiment. One hundred and sixty samples are collected, categorized labeled and stored in a database. The feature vector is produced for all words and formed a training set for classification and recognition purpose. A sequence of decomposition levels are carried out to achieve a good feature vector. A feature vector of element size twelve is collected for all words at the eighth level of decomposition. A graph is also prepared for the comparison of the signal at each decomposition level. From that graph we can understand the physical changes that are occurred during the decomposition. By using this method we have achieved a recognition rate of 89%.