Segmental Lvq Training for Phoneme Wise Tied Mixture Density Hmms
نویسنده
چکیده
This work presents training methods and recogni tion experiments for phoneme wise tied mixture den sities in hidden Markov models HMM The system trains speaker dependent but vocabulary independent phoneme models for the recognition of Finnish words The Learning Vector Quantization LVQ methods are applied to increase the discrimination between the phoneme models A segmental LVQ training is pro posed to substitute the LVQ based corrective tuning as a parameter estimation method The experiments indi cate that the new method can provide the corresponding recognition accuracy but with less training and more robustness over the initial models Experiments to up scale the current system by introducing context vectors and larger mixture pools show up to reduction of recognition errors compared to the earlier results in
منابع مشابه
Hybrid Training Method for Tied Mixture Density Hidden Markov Models Using Learning Vector Quantization and Viterbi Estimation
In this work the output density functions of hidden Markov models are phoneme-wise tied mixture Gaussians. For training these tied mixture density HMMs, modiied versions of the Viterbi training and LVQ based corrective tuning are described. The initialization of the mean vectors of the mixture Gaussians is performed by rst composing small Self-Organizing Maps representing each phoneme and then ...
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