Abstract: A natural number recognition method and system that uses minimum classification error trained inter-word context dependent models of the head-body-tail type over a specific vocabulary. One part of the method and system allows recognition of spoken monetary amounts in financial transactions. A second part of the method and system allows recognition of numbers such as credit card or U.S. telephone numbers. A third part of the method and system allows recognition of natural language expressions of time, such as time of day, day of the week and date of the month for applications such as scheduling or schedule inquires. Even though limited natural language expressions are allowed, context sharing between similar sounds in the vocabulary within a head-body-tail model keeps storage and processing time requirements to manageable levels.

Abstract: A new speaker verification method, termed Mixture Decomposition Discrimination (MDD), and a new apparatus for using MDD are presented. MDD takes mixture component score information from a speaker independent recognizer and transmits this information while it is still decomposed as a mixture of component scores that indicate the response of the states of a Hidden Markov Model (HMM) before this information is combined into a single speaker independent recognizer parameter. MDD can be very effective in improving the performance of existing verification methods based on speaker dependent HMMs with cohort normalization because the errors of the two speaker verification methods are very uncorrelated statistically. Experimental results have shown that when MDD is incorporated into a system that also uses speaker dependent HMMs, the resulting hybrid system has its average equal error rate reduced by 46% compared to cohort normalized speaker independent HMMs.