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Novel Applications of Complexity Inspired RDT Transform for Low Complexity Embedded Speech Recognition in Automotive Environments

Embedded dictation, i.e. recognizing vocal commands in noisy environments, with good accuracy and using low complexity implementations is a desirable task with many applications. Such applications include automotive infotainment solutions particularly when no connectivity is available, personal assistants including embedded dictation solutions for disabled people, and so on. This paper reports our novel results in applying a nonlinear transform (RD-transform) introduced in a previous work and inspired form complexity measurements of signals generated in cellular automaton. Such a transform is compact and has a low computational complexity yet it previously proved quite efficient in terms of accuracy for a standard task of recognizing user independent dictation of digits. Herein, we report results on employing RD-transform on a specially designed sound database containing commands for the non-critical automotive equipment in a realistic, noisy environment. In addition to specific nonlinear transforms, low complexity FSVC classifiers were employed proving that good accuracies can be achieved using a very convenient implementation solution.