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In the face of too lower recycling rate of end-of-life vehicles (ELV) in China, we establish an optimized recycling price model to enhance recycling rate through constrained nonlinear programming. The solution of this model is obtained using Lagrangian relaxation algorithm. The performance of the proposed solution method has been demonstrated with computational experimentation. Furthermore, this paper...
In the past several decades, classifier-independent front-end feature extraction, where the derivation of acoustic features is lightly associated with the back-end model training or classification, has been prominently used in various pattern recognition tasks, including automatic speech recognition (ASR). In this paper, we present a novel discriminative feature transformation, named generalized likelihood...
Linear discriminant analysis (LDA) is designed to seek a linear transformation that projects a data set into a lower-dimensional feature space while retaining geometrical class separability. However, LDA cannot always guarantee better classification accuracy. One of the possible reasons lies in that its formulation is not directly associated with the classification error rate, so that it is not necessarily...
In this paper, we consider extractive summarization of broadcast news speech and propose a unified probabilistic generative framework that combines the sentence generative probability and the sentence prior probability for sentence ranking. Each sentence of a spoken document to be summarized is treated as a probabilistic generative model for predicting the document. Two matching strategies, namely...
Linear discriminant analysis (LDA) is designed to seek a linear transformation that projects a data set into a lower-dimensional feature space for maximum class geometrical separability. LDA cannot always guarantee better classification accuracy, since its formulation is not in light of the properties of the classifiers, such as the automatic speech recognizer (ASR). In this paper, the relationship...
This paper considers word position information for language modeling. For organized documents, such as technical papers or news reports, the composition and the word usage of articles of the same style are usually similar. Therefore, the documents can be separated into partitions consisting of identical rhetoric or topic styles by the literary structures, e.g., introductory remarks, related studies...
The performance of current automatic speech recognition (ASR) systems often deteriorates radically when the input speech is corrupted by various kinds of noise sources. Quite a few of techniques have been proposed to improve ASR robustness over the last few decades. Related work reported in the literature can be generally divided into two aspects according to whether the orientation of the methods...
This paper considers minimum phone error (MPE) based discriminative training of acoustic models for Mandarin broadcast news recognition. A novel data selection approach based on the normalized frame-level entropy of Gaussian posterior probabilities obtained from the word lattice of the training utterance was explored. It has the merit of making the training algorithm focus much more on the training...
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