Fundamentals of speech recognition
Fundamentals of speech recognition
Recognizing speech of goats, wolves, sheep and...non-natives
Speech Communication
Intelligibility of native and non-native Dutch speech
Speech Communication
Maximum a posteriori adaptation for large scale HMM recognizers
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Rapid speech recognizer adaptation to new speakers
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
A generalization of the maximum a posteriori training algorithm for mixture priors
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Towards language independent acoustic modeling
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
A study on speaker adaptation of the parameters of continuousdensity hidden Markov models
IEEE Transactions on Signal Processing
Universal coding, information, prediction, and estimation
IEEE Transactions on Information Theory
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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.