| Abstrakt: |
Recently, a great deal of interest has been shown in performing reliable speech recognition in an automotive environment. In order to determine the level of recognition accuracy to be expected in such a hostile environment, a series of experiments was performed. The data base for these experiments was created by having four talkers (two male, two female) speak ten replications of an 11-digit vocabulary, zero-nine and oh (in an isolated format), while driving in an automobile. Five different environmental conditions were studied: (1) car off, fan off; (2) car idling, fan on high; (3) car traveling at 30 mph, fan on high; (4) car traveling at 60 mph, fan on high; and (5) car traveling at 60 mph, fan off. Two different recognition algorithms were tested, namely; (1) standard LPS-based template recognition, using dynamic time warping; and (2) continuous density hidden Markov model recognition, using Viterbi scoring and cepstral analysis. Several different distance measures were also evaluated for each of the recognition sytems. Results from the template-based algorithms showed speaker-dependent recognition accuracies from 96%-99% depending on which condition was used for training. A five-state HMM system yielded recognition accuracies from 93%-98% depending on the training condition. [ABSTRACT FROM AUTHOR] |
