Browsing by Author "Cheng, Shih-Sian"

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A prototypes-embedded genetic K-means algorithm
(2006)
Cheng, Shih-Sian
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Chao, Yi-Hsiang
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Wang, Hsin-Min
;
Fu, Hsin-Chia
;
資訊工程學系
This paper presents a genetic algorithm (GA) for K-means clustering. Instead of the widely applied string-of-group-numbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The one-step K-means algorithm is used as the mutation operator. Hence, the proposed GA is called the prototypes-embedded genetic K-means algorithm (PGKA). With the inherent evolution process of evolutionary algorithms, PGKA has superior performance than the classical K-means algorithm, while comparing to other GA-based approaches, PGKA is more efficient and suitable for large scale data sets.
Automatic construction of regression class tree for MLLR via model-based hierarchical clustering
(2006)
Cheng, Shih-Sian
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Xu, Yeong-Yuh
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Wang, Hsin-Min
;
Fu, Hsin-Chia
;
資訊工程學系
In this paper, we propose a model-based hierarchical clustering algorithm that automatically builds a regression class tree for the well-known speaker adaptation technique - Maximum Likelihood Linear Regression (MLLR). When building a regression class tree, the mean vectors of the Gaussian components of the model set of a speaker independent CDHMM-based speech recognition system are collected as the input data for clustering. The proposed algorithm comprises two stages. First, the input data (i.e., all the Gaussian mean vectors of the CDHMMs) is iteratively partitioned by a divisive hierarchical clustering strategy, and the Bayesian Information Criterion (BIC) is applied to determine the number of clusters (i.e., the base classes of the regression class tree). Then, the regression class tree is built by iteratively merging these base clusters using an agglomerative hierarchical clustering strategy, which also uses BIC as the merging criterion. We evaluated the proposed regression class tree construction algorithm on a Mandarin Chinese continuous speech recognition task. Compared to the regression class tree implementation in HTK, the proposed algorithm is more effective in building the regression class tree and can determine the number of regression classes automatically.
BIC-based audio segmentation by divide-and-conquer
(2008)
Cheng, Shih-Sian
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Wang, Hsin-Min
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Fu, Hsin-Chia
;
資訊工程學系
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Department of Computer Science
Audio segmentation has received increasing attention in recent years for its potential applications in automatic indexing and transcription of audio data. Among existing audio segmentation approaches, the BIC-based approach proposed by Chen and Gopalakrishnan is most well-known for its high accuracy. However, this window-growingbased segmentation approach suffers from the high computation cost. In this paper, we propose using the efficient divide-and-conquer strategy in audio segmentation. Our approaches detect acoustic changes by recursively partitioning an analysis window into two sub-windows using Delta BIC. The results of experiments conducted on the broadcast news data demonstrate that our approaches not only have a lower computation cost but also achieve a higher segmentation accuracy than window-growing-based segmentation.
BIC-Based Speaker Segmentation Using Divide-and-Conquer Strategies With Application to Speaker Diarization
(2010-01-01)
Cheng, Shih-Sian
;
Wang, Hsin-Min
;
Fu, Hsin-Chia
;
資訊工程學系
;
Department of Computer Science
In this paper, we propose three divide-and-conquer approaches for Bayesian information criterion (BIC)-based speaker segmentation. The approaches detect speaker changes by recursively partitioning a large analysis window into two sub-windows and recursively verifying the merging of two adjacent audio segments using Delta BIC, a widely-adopted distance measure of two audio segments. We compare our approaches to three popular distance-based approaches, namely, Chen and Gopalakrishnan's window-growing-based approach, Siegler et al.'s fixed-size sliding window approach, and Delacourt and Wellekens's DISTBIC approach, by performing computational cost analysis and conducting speaker change detection experiments on two broadcast news data sets. The results show that the proposed approaches are more efficient and achieve higher segmentation accuracy than the compared distance-based approaches. In addition, we apply the segmentation approaches discussed in this paper to the speaker diarization task. The experiment results show that a more effective segmentation approach leads to better diarization accuracy.
Model-Based Clustering by Probabilistic Self-Organizing Maps
(2009-05-01)
Cheng, Shih-Sian
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Fu, Hsin-Chia
;
Wang, Hsin-Min
;
資訊工程學系
;
Department of Computer Science
In this paper, we consider the learning process of a probabilistic self-organizing map (PbSOM) as a model-based data clustering procedure that preserves the topological relationships between data clusters in a neural network. Based on this concept, we develop a coupling-likelihood mixture model for the PbSOM that extends the reference vectors in Kohonen's self-organizing map (SOM) to multivariate Gaussian distributions. We also derive three expectation-maximization (EM)-type algorithms, called the SOCEM, SOEM, and SODAEM algorithms, for learning the model (PbSOM) based on the maximum-likelihood criterion. SOCEM is derived by using the classification EM (CEM) algorithm to maximize the classification likelihood; SOEM is derived by using the EM algorithm to maximize the mixture likelihood; and SODAEM is a deterministic annealing (DA) variant of SOCEM and SOEM. Moreover, by shrinking the neighborhood size, SOCEM and SOEM can be interpreted, respectively, as DA variants of the CEM and EM algorithms for Gaussian model-based clustering. The experimental results show that the proposed PbSOM learning algorithms achieve comparable data clustering performance to that of the deterministic annealing EM (DAEM) approach, while maintaining the topology-preserving property.