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Our aim in this paper is to propose a rule-weight learning algorithm in fuzzy rule-based classifiers. The proposed algorithm is presented in two modes: first, all training examples are assumed to be equally important and the algorithm attempts to minimize the error-rate of the classifier on the training data by adjusting the weight of each fuzzy rule in the rule-base, and second, a weight is assigned...
We present a semi-supervised learning (SSL) method for building domain-specific language models (LMs) from general-domain data using probabilistic latent semantic analysis (PLSA). The proposed technique first performs topic decomposition (TD) on the combined dataset of domain-specific and general-domain data. Then it derives latent topic distribution of the interested domain, and derives domain-specific...
In this paper, we explore the generalization capability of acoustic model for improving speech recognition robustness against noise distortions. While generalization in statistical learning theory originally refers to the model's ability to generalize well on unseen testing data drawn from the same distribution as that of the training data, we show that good generalization capability is also desirable...
We present a semi-supervised learning method for building domain-specific language models (LM) from general-domain data. This method is aimed to use small amount of domain-specific data as seeds to tap domain-specific resources residing in larger amount of general-domain data with the help of topic modeling technologies. The proposed algorithm first performs topic decomposition (TD) on the combined...
In this paper, we propose a method to extend the use of latent topics into higher order n-gram models. In training, the parameters of higher order n-gram models are estimated using discounted average counts derived from the application of probabilistic latent semantic analysis(PLSA) models on n-gram counts in training corpus. In decoding, a simple yet efficient topic prediction method is introduced...
We propose novel approaches for optimizing the detection performance in spoken language recognition. Two objective functions are designed to directly relate model parameters to two performance metrics of interest, the detection cost function and the area under the detection-error-tradeoff curve, respectively. Both metrics are approximated with differentiable functions of model parameters by using...
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