Heyun Huang

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Concatenating sequences of feature vectors helps to capture essential information about articulatory dynamics, at the cost of increasing the number of dimensions in the feature space, which may be characterized by the presence of manifolds. Existing supervised dimensionality reduction methods such as Linear Discriminant Analysis may destroy part of that(More)
Automatic Speech Recognition (ASR) depends crucially on establishing acoustic models for speech units including phones. One disadvantage that lies in popular acoustic models is the lack of modeling speech continuity information. Stacking short-term features of consecutive frames may keep sufficient articulatory information. Unfortunately, the resultant(More)
Modeling the second-order statistics of articulatory trajectories is likely to improve the performance in classifying phone segments compared to using only linear combinations of MFCCs. Nevertheless, the extremely high dimensionality of the feature space spanned by a combination of monomials of degree-1 and degree-2 makes it difficult to effectively exploit(More)
The use of higher-order polynomial acoustic features can improve the performance of automatic speech recognition. However, the dimensionality of the polynomial representation can be prohibitively large, making the training of acoustic models using polynomial features for large vocabulary ASR systems infeasible. This paper presents an iterative polynomial(More)
The articulators of human speech might only be able to move slowly, which results in the gradual and continuous change of acoustic speech properties. Nevertheless, the so-called speech continuity is rarely explored to discriminate different phones. To exploit this, this paper investigates a multiple-frame MFCC representation (that is expected to retain(More)
Using contextual information of phones is an effective way to improve the performance of phone classification tasks, but requires the use of dimensionality reduction. One of the disadvantages of Linear Discriminant Analysis (LDA), a popular dimensionality reduction method is that it is not able to account for local differences between the distributions of(More)
Since the emergence of peer-to-peer (P2P) networking in the last 90s, P2P traffic, being a significant portion of the network traffic today, has constituted a highly desirable class for identification. How to improve the accuracy of the P2P traffic identification efficiently is still a challenging problem. The support vector machine (SVM) is a powerful(More)
We have developed a new set of lyophilized kits, composed of 3 different kits, for the instant preparation of no-carrier-added 131 I-MIBG in the clinic. We here discussed the formulation of the kits, optimization of radiolabelling, quality control of radiolabeled 131 I-MIBG, and studies of animal biodistribution. The no-carrier-added (nca) 131 I-MIBG(More)