Speech recognition system

Automatic Speech Recognition (ARS) has progressed considerably over the past several decades, but still has not achieved the potential imagined at its very beginning. Almost all of the existing applications of ASR systems are PC based. This thesis is an attempt to develop a speech recognition system that is independent of any PC support and is small enough in size to be used in a daily use consumer appliance. This system would recognize isolated utterances from a limited vocabulary, provide speaker independence, require less memory and be cost-efficient compared to present ASR systems. In this system, speech recognition is performed with the help of algorithms such as Vector Quantization and Zero Crossing. Several features of a Digital Signal Processor (DSP) have been utilized to generate and execute the algorithms for recognition. The final system has been implemented on Texas Instmments TMS320C30 DSP. The system, when implemented using the Vector Quantizer approach, achieved an accuracy of 94% for a vocabulary of 6 words and a recognition time of 6 seconds. The zero crossing approach resulted in an accuracy of 89% for the same vocabulary while the recognition time was 0.8 seconds.