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Predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient human document review. Previously, the sole front-end input used to create a predictive model was the exemplar documents (training data) chosen by subject-matter experts. Many predictive...
On the basis of the heavy metal pollution datum tested in 162 months uninterruptedly of a tailings pond effluent, this paper studied the rules of heavy metal pollution by applying mathematic statistics method. And the artificial neural network based on the improved BP algorithm is applied to predict the heavy metal ions' concentration of tailings pond effluent so as to further disclose the pollution...
In this paper, we propose an automatic data-driven technique for selecting proper background dataset. By the technique, impostor confidence(IC) is proposed as a metric and more discriminative background dataset is automatically chose by impostor confidence(IC) to train more discriminative model. Experiment results on NIST 2008 SRE corpus in GMM-SVM speaker verification system show that the proposed...
The paper presents a support vector machine based Part-Of-Speech tagging on Chinese database which is part of our speech synthesis system. The model can be classified as SVM model and uses many sequential features to predict the POS tag. The text database was download from the internet with 1,280,000 words and 33 parts of Speech. The total accuracy of our experiments is 99.31%.
Recently, using maximum likelihood linear regression (MLLR) transforms as the features for SVM based speaker recognition has been proposed. This can achieve performance comparable to that obtained with state-of-the-art approaches. In this paper, we focus on calculating the transforms based on a GMM universal background model (UBM). Rather than estimating the transforms using maximum likelihood criterion,...
Token-based approaches have proven quite effective for spoken language identification (LID). Traditionally, Speech utterances are first decoded into token sequences, and then LID tasks are performed on these token sequences by either n-gram language models or support vector machines. In this paper, we propose a hierarchical system design, which utilizes a group of bayesian logistic regression models...
In this paper, a novel statistical method based on conditional random fields (CRF) is proposed for hierarchical prosody structure prediction, which is a key module in speech synthesis systems. We will discuss how to build the prosody models for mandarin Chinese using conditional random fields in detail, including corpus preparation, feature selection, feature template design, model training and evaluation...
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