Integrating neural nets and one-stage dynamic programming for speaker independent continuous Mandarin digit recognition

A Bayesian neural network; a one-stage dynamic programming algorithm, and a Hopfield time-alignment network are integrated to form a speaker-independent continuous Mandarin digit recognizer. In this system, a Bayesian network trained with a splitting LVQ (learning vector quantisation) and the LVQ2 algorithms gives the a posteriori probability. The one-stage algorithm is then employed for coarse recognition. Finally, the Hopfield time-alignment network is used to eliminate unreasonable candidates. Experimental evaluation of this system, using 53 speakers (28 male, 25 female), each speaking 20 digit strings of varying length (1-7 digits/string) and at varying speaking rate (150-240 digits/min), gave an average recognition accuracy of 94.3%, with 1.4% insertion, 1.1% deletion, and 3.2% substitution errors