Using Text-to-Speech Engine to Improve the Accuracy of a Speech-Enabled Interface

This paper presents an automatic user profile building and training (AUPB&T) system for speech recognition. This system uses text-to-speech (TTS) voices to improve the language models and the performance of current commercial automatic speech recognition (ASR) engines. The vocabularies of these systems are usually suited for general usage. Users have no easy means of training these engines. They generally shun the proposed training methods that require long and picky training sessions. Our proposed solution is a system that accepts the user documents and favorite Web pages, and feeds them to a (TTS) module in order to improve the accuracy of spoken information retrieval queries. The results show that AUPB&T considerably improves the recognition engine performance of the Microsoft speech recognition system without having to resort to manual training.