In this paper, an improved method of model complexity selection for nonnative speech recognition is proposed by using maximum a posteriori estimation of bias distributions. An algorithm is described for estimating the hyper-parameters of the prior distributions, and an automatic accent detection algorithm is also proposed for integration with dynamic model selection and adaptation. Experiments were performed on the WSJ 1 task with American English speech, British accent speech, and mandarin Chinese accent speech. Results show that the use of prior knowledge of accents enabled reliable estimation of bias distributions in the case of very small amount of adaptation speech, or without adaptation speech. Recognition results show that the new approach is superior to the previous MEL method, especially when the adaptation data are extremely limited.
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