The Chinese archive document possesses special geometrical and logical properties due to its construction based upon rectangular field which contain either title strings or data strings related to some other titles. In this paper, we propose a knowledge-based approach to analyze the logical relationship among the fields. After extracting the lines and fields of an archive document image, this procedure can identify fields as the title fields, the sub-title fields (if there exist such tree-structure logical relationship), and the corresponding data fields. This proposed approach enables us to achieve a better performance in information manipulation of archive documents.

In a traditional speech recognition system, the distance score between a test token and a reference pattern is obtained by simply averaging the distortion sequence resulted from the matching of the two patterns through a dynamic programming procedure. The final decision is made by choosing the one with the minimal average distance score. If one views the distortion sequence as a form of observed features, a decision rule based on a specific discriminant function designed for the distortion sequence obviously will perform better than that based on the simple average distortion. The authors therefore, suggest a linear discriminant function of the form triangle = Sigma /sub (i1)/T w(i)* d(i) to compute the distance score triangle instead of a direct average triangle =1/T Sigma /sub (i1)/T d(i). Several adaptive algorithms are proposed to learn the discriminant weighting function. These include one heuristic method, two methods based on the error propagation algorithm, and one method based on the generalized probabilistic descent algorithm (GPD). They study these methods in a speaker-independent speech recognition task involving utterances of the highly confusible English E-set (b,c,d,e,g,p,t,v,z). The results show that the best performance is obtained by using the GPD-method which achieved a 78.1% accuracy, compared to 67.6% with the traditional unweighted average method. Besides the experimental comparisons, an analytical discussion of various training algorithms is also provided.>

A new minimum recognition error formulation and a generalized probabilistic descent (GPD) algorithm are analyzed and used to accomplish discriminative training of a conventional dynamic-programming-based speech recognizer. The objective of discriminative training here is to directly minimize the recognition error rate. To achieve this, a formulation that allows controlled approximation of the exact error rate and renders optimization possible is used. The GPD method is implemented in a dynamic-time-warping (DTW)-based system. A linear discriminant function on the DTW distortion sequence is used to replace the conventional average DTW path distance. A series of speaker-independent recognition experiments using the highly confusible English E-set as the vocabulary showed a recognition rate of 84.4% compared to approximately 60% for traditional template training via clustering. The experimental results verified that the algorithm converges to a solution that achieves minimum error rate.>

A newly proposed minimum recognition error formulation and a generalized probabilistic descent (GPD) algorithm are analyzed and used to accomplish discriminative training of a conventional dynamic programming based speech recognizer. Unlike many other approaches, the objective of discriminative training the new framework is to directly minimize the recognition error rate. A series of speaker independent recognition experiments using the highly confusing English E-set as the vocabulary was conducted to examine the characteristics of the GPD method for discriminative training. Without ad hoc supplementary schemes, the method achieved a recognition rate of 83.7%, a remarkable performance improvement compared to 63.8% with the traditional template training via clustering. The experimental results verify that the GPD algorithm with the new minimum recognition error formulation indeed converges to a solution that accomplishes the objective of minimum error rate.>

The authors suggest a linear discriminant function to complete the distance score instead of a conventional average distance. Several discriminative algorithms are proposed to learn the discriminant function. These include one heuristic method, two methods based on the error propagation algorithm, and one method based on the generalized probabilistic descent (GPD) algorithm. The authors study these methods in a speaker-independent speech recognition task involving utterances of the highly confusable English E-set. The results show that the best performance is obtained by using the GPD method, which achieved a 78.1% accuracy, compared to 67.6% with the traditional average method.>

Tone recognition of isolated Mandarin monosyllables using the multilayer perceptron (MLP) model is reported. Ten features extracted from the fundamental frequency and energy contours of a monosyllable are used as the recognition features. The backpropagation algorithm is used to train the internal representation of the MLP. Several variations of the MLP with regard to the number of layers and the number of neurons in each layer are considered. In a speaker-untrained test, a recognition rate of 93.8% was achieved. It outperforms a Gaussian classifier which has a recognition rate of 90.6%.>