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This paper describes an implementation of a speaker-independent digit recognition system. The digit classification scheme is based on segmenting the unknown word into three regions and then making categorical judgments as to which of six broad acoustic classes each segment falls into. The measurements made on the speech waveform include energy, zero crossings, and two-pole LPC analysis. A formal evaluation of the systems showed an error rate of 2.7% for a carefully controlled recording environment, and a 5.6% error rate for online recordings in a noisy computer room.
Sambur et al. (Fri,) studied this question.