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This paper presents our recent work towards the development of a train query system based on speech recognition, text correction and natural language processing. The system enhances the accuracy of speech recognition by combining rules and word frequencies. This paper summarizes and classifies the error types of speech recognition in the field of train query, and then brings forth a semantic framework and a word-frequency-precedence mechanism with pinyin-text mapping. For the text after correctly recognizing, the system extracts the origin and the destination by forward-search-backward-match algorithm. And it synthesizes the least transferring algorithm and the shortest path algorithm to find a route. So it has characteristics of humanity and intelligence.

A Train Query System Based on Speech Recognition and Text Correction