Robust Self-Training System for Spoken Query Information Retrieval using Pitch Range Variations

This paper presents an automatic user profile building and training (AUPB&T) system using voice pitch variation for speech recognition engines. The problem with current ASR engines is that their vocabularies are usually only suited for general usage. Another problem with current ASR engines is that there is no easy means for visually challenged users to train the engine to improve its performance. Our proposed solution consists of a system that can accept a user's document and favorite Web pages. These documents can then be parsed and their words added to the ASR engine's lexicon. Next, it uses those documents to start an ASR training session. The training can completed automatically by using a high quality text-to-speech (TTS) natural voice. In order to overcome the problem of the limited number of high quality natural TTS voices available, we propose to integrate voice pitch variation during the training phase of AUPB&T, which can cover a broader range of user variability. The results of our experiments using standard ASR and TTS engines show that the AUPB&T system using pitch variation improved the recognition rate for an unknown beta speaker