Prior knowledge guided maximum expected likelihood based model selection and adaptation for nonnative speech recognition
In this paper, an improved method of model complexity selection for nonnative speech recognition is proposed by using maximum a posteriori (MAP) estimation of bias distributions. An algorithm is described for estimating hyper-parameters of the priors of the bias distributions, and an automatic accent classification algorithm is also proposed for integration with dynamic model selection and adaptation. Experiments were performed on the WSJ1 task with American English speech, British accented speech, and mandarin Chinese accented speech. Results show that the use of prior knowledge of accents enabled more reliable estimation of bias distributions with very small amounts of adaptation speech, or without adaptation speech. Recognition results show that the new approach is superior to the previous maximum expected likelihood (MEL) method, especially when adaptation data are very limited.
maximum expected likelihood model selection nonnative speech recognition accent classification maximum a posteriori estimation