Automatic continuous speech recogniser for Dravidian languages using the auto associative neural network

In recent times with the extensive improvement of computers, numerous methods of data interchange between man and computer are revealed. It aims to provide an efficient way for human to communicate with computers exclusively for people with disabilities who face diversity of obstacles while using computers. This paper predominantly focuses on developing an efficient speech recognition system for Dravidian languages such as Tamil, Malayalam, Telugu and Kannada. The proposed CSR system comprises of four steps namely pre-processing, feature extraction, automatic continuous speech segmentation and classification. The most powerful and widely used short term energy and zero crossing rate is used for continuous speech segmentation and Mel frequency cepstral coefficients (MFCC), linear predictive cepstral coefficients (LPCC) and shifted delta cepstrum (SDC) feature extractions are used for recognition system. Experiments are carried out with real time Dravidian languages speech signal. It is observed from the results that the proposed system gives significant results in AANN classifier with MFCC feature when compared with LPCC and SDC features.