The widespread acceptance of personal digital assistants (PDAs) has led to research into the interaction people have with these devices. Speech is a natural choice for this interaction. However, traditional speech recognition systems require an abundance of memory and processor cycles. On limited machines like an iPAQ, implementing an entire speech recognition system would be debilitating to the device. A solution to this problem is to allow the iPAQ to communicate with a server that does the actual recognition task. This method is better known as distributed speech recognition (DSR). This thesis examines the problems of implementing DSR on an iPAQ. We faced the challenge of reducing the bandwidth required by the system while maintaining reasonable recognition error rates. We examined using a fixed-point processing in reducing the computational demand put on the iPAQ. The word error rates for the baseline floating-point front-end system and our fixed-point front-end were 9.8% and 9.6% respectively. However, using the fixed-point front-end actually increased our bit rate. Next, we focused on the effects of quantizing Mel-Frequency Cepstral Coefficients (MFCCs) before sending them to a recognizer on the server side. We evaluated both scalar and vector quantizers using non-uniform bit allocation. Our optimal vector quantizer reached a word error rate of 9.8% at 6400 bps. Finally, because our recognizer further processes the MFCCs to arrive at boundary measurements, we explored the idea of quantizing these boundary measurements. The scalar boundary measurement quantizer reached a word error rate of 9.6% at 150 bits per hypothesized boundary. We averaged 21.1 hypothesize boundaries per second on our test data; thus, we could transmit boundary measurements at 3165 bps and maintain a 9.6% word error rate. Thesis Supervisor: I. Lee Hetherington Title: Research Scientist
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