A novel real-time algorithm has been developed for estimating temporal word boundaries in measured speech without the need for interpretation of individual words. This algorithm is the foundational building block of a method for estimating a variety of key metrics such as word production rate, phrase production rate, words per phrase, etc., that are indicative of human mental states. In particular, we are interested in developing a system for monitoring locomotive crew alertness. The majority of existing speech processing algorithms relies on pre-recorded speech corpora. The real-time algorithm presented here is unique in that it employs a simple and efficient pattern matching method to identify temporal word boundaries by monitoring threshold crossings in the speech power signal. This algorithm eliminates the need to interpret the speech, and still produces reasonable estimates of word boundaries. The proposed algorithm has been tested with a batch of experimentally recorded speech data and with real time speech data. The results from the testing are outlined in this paper.

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