Improving speech recognition accuracy by means of word pronunciation modeling
Background: Pronunciation variation modeling evidently has a big potential as a simple way to significantly improve the accuracy of automatic speech recognition. At the same time, the reported improvements in accuracy obtained with pronunciation variation models in experiments are still far from the expected ones. Methods: The advantages of the so-called explicit pronunciation variation models are explored as an approach for improvement of natural Russian speech recognition accuracy. The probabilistic pronunciation variation model is formally defined as well as the methods of its parameter estimation. Results: The effect of use of explicit pronunciation variation models is shown to be very dependent on the speech material type. Evaluation on the corpus with Russian read and planned speech shows a negligible effect of using the models. At the same time, the evaluation of pronunciation models on spontaneous Russian speech reveals substantial improvement of automatic speech recognition accuracy. Conclusions: Despite big promises, there are a lot of efforts necessary to develop pronunciation variation models for speech recognition that will effectively account for speaker and speaking style, accents, and dialects. Nevertheless, right now, the pronunciation model of explicit type can show substantial improvement of recognition accuracy on natural speech recognition task.