Speaker independent real-time speech recognition system

This thesis attempts to develop a real-time speaker-independent Automatic Speech Recognition (ASR) system. The system recognizes isolated utterances from a limited vocabulary, and is small and cost-efficient to be incorporated into a consumer appliance. The recognition is based on zero crossings and energy content measurement on the speech waveforms. The algorithm is based on segmenting the speech waveform into ten equally spaced intervals and performing a match with the patterns in a reference template. The system was implemented on an IBM Personal Computer and achieved an error rate of 0% on a vocabulary of four words from an initial ten-word database of 16 speakers (8 male and 8 female). The system recognized unknown utterances in less than 0.3 seconds.