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 is in the recognition of numbers such as credit card or U.S. telephone numbers. A third part is the 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. Very excellent error rates have been achieved over more traditional context independent whole-word models. Further, context sharing in a head-body-tail model between similar sounds in the vocabulary helps to greatly reduce storage and processing requirements. This keeps speech recognition units based on such methods and systems be more acceptable from monetary and temporal cost considerations. <IMAGE>
Context sharing for similar words in context dependent hidden markov models