An Efficient Continuous Speech Recognition System for Dravidian Languages Using Support Vector Machine

This paper mainly focuses on developing a novel speech recognition system for Dravidian languages such as Tamil, Malayalam, Telugu, and Kannada. This research work targets to afford a well-organized way for human to interconnect with computers absolutely for people with disabilities who facade variety of stumbling blocks while using computers. This work would be very helpful to the native speakers in various applications. The proposed CSR system comprises of three steps namely preprocessing, feature extraction, and classification. In the preprocessing step, the input signal is preprocessed through the steps such as pre-emphasis filter, framing, windowing, and band stop filtering in order to remove the background noise and to enrich the signal. The best-filtered and the enriched signal from the preprocessing step is taken as the input for the further process of CSR system. The speech features being the most essential segment in speech recognition system. The most powerful and widely used short-term energy (STE) and zero-crossing rate (ZCR) are used for continuous speech segmentation, and Mel-frequency cepstral coefficients (MFCC) and shifted delta cepstrum (SDC) are used for recognition task. Feature vectors are given as the input to the classifier such as support vector machine (SVM) for classifying and recognizing Dravidian language speech. Experiments are carried out with real-time Dravidian speech signals, and the results reveal that the proposed method competes with the existing methods reported in literature.