This paper proposes a solution for improving the safety of rail and other mass transportation systems through operator alertness monitoring. A non-invasive method of alertness monitoring through speech processing is presented. Speech analysis identifies measurable vocal tract changes due to fatigue and decreased speech rate due to decreased mental ability. Enabled by existing noise reduction technology, a system has been designed for measuring key speech features that are believed to correlate to alertness level. The features of interest are pitch, word intensity, pauses between words and phrases, and word rate. The purpose of this paper is to describe the overall alertness monitoring system design and then to show some experimental results for the core processing algorithm which extracts features from the speech. The feature extraction algorithm proposed here uses a new and simple technique to parse the continuous speech signal coming from the communication signal without using computationally demanding and error-prone word recognition techniques. Preliminary results on the core feature extraction algorithm indicate that words, phrases, and rates can be determined for relatively noise-free speech signals. Once the remainder of the overall alertness monitoring system is complete, it will be applied to real life recordings of train operators and will be subjected to clinical testing to determine alert and non-alert levels of the speech features of interest.

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