Improvements in connected digit recognition using higher order spectral and energy features

It is shown how one can apply the improved acoustic modeling techniques (using a continuous density hidden Markov model framework) developed for large vocabulary speech recognition applications to the problem of connected digit recognition with no changes made to the basic modeling techniques and with no vocabulary specific information used. The improved modeling techniques adopted in this study include an improved feature analysis procedure, which incorporates higher order cepstral and log energy time derivatives, and an improved acoustic resolution procedure, which uses more Gaussian mixture components per state to characterize the acoustic variability in each state of the model. Using these techniques, string accuracies of 98.6% for unknown length strings and 99.2% for known length strings were achieved on the standard Texas Instruments connected digits database