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A key challenge in rapidly building Tibetan language speech recognition applications is minimizing the manual effort required in transcribing and labeling speech data. Accurate labeling of Tibetan speech utterances is extremely time consuming and requires trained linguists. For alleviate this problem, we present an approach that aims at reducing the amount of manually transcribed speech data required...
In the researches on Tibetan language speech recognition, accurate labeling of Tibetan speech utterances is extremely time consuming and requires trained linguists. For alleviate this problem, we present an approach that can use few labeled Tibetan speech utterances to construct the effective recognition model. The experimental results show that our approach has better performance than traditional...
MBBNTree algorithm, which integrates the advantage of Markov blanket Bayesian networks (MBBN) and decision tree, would behave better performance than other Bayesian networks for classification. But the available training samples with actual classes are not enough for building MBBNTree classifier in practice. Active learning aims at reducing the number of training examples to be labeled by automatically...
The available cases with actual classes are not enough for building telecom clientspsila credit classification model in practice, especially for the newly established system in which old customerspsila data do not exist. For evaluating telecom clientspsila credit, a classifier based on active learning is proposed in this paper. Active learning aims at reducing the number of training examples to be...
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