Speech recognition using weighted HMM and subspace projection approaches

Su, Keh-Yih; Lee, Chin‐Hui

doi:10.1109/89.260336

Cited by 43 publications

(2 citation statements)

References 30 publications

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“…, then sort descending ( ) C k according to their value and select l features with the best value to form the optimal eigenvector. To make the system more robust, we introduce the cross correlation coefficient [13] to measure the correlation between the two features.…”

Section: Feature-level Data Fusionmentioning

confidence: 99%

Study of Data Fusion Method for Fault Diagnosis Based on FDR Feature Selection Algorithm and HMM/SVM Model

Zhang

2012

AMR

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To effectively avoid the loss of useful information, in this paper, we extract feature information from the fault signal of rotating machinery in different aspects such as amplitude-domain, time-domain and time-frequency domain. Then for the multi-dimensional feature extraction is prone to the problem of “dimension disaster”, introduce the principles of FDR in data mining to determine the classification ability of each individual feature, and introduce the cross correlation coefficient to solve the problem that dealing with individual feature neglects the interrelationship between the features, and construct a new feature level data fusion algorithm. Finally, According to the characteristics of the HMM (Hidden Markov model), SVM (Support Vector Machine) and its hybrid model, we construct a new decision-level data fusion model.

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Section: Feature-level Data Fusionmentioning

confidence: 99%

Study of Data Fusion Method for Fault Diagnosis Based on FDR Feature Selection Algorithm and HMM/SVM Model

Zhang

2012

AMR

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“…To make the system more robust. In this paper, we introduce the cross correlation coefficient [5] to measure the correlation between the two features.…”

Section: Feature Selectionmentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery Based on FDR Feature Selection Algorithm and SVM

Zhang

Xia

2010

AMR

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To effectively avoid the loss of useful information, in this paper, feature information has been extracted from the fault signal of rotating machinery in different aspects such as amplitude-domain, time-domain and time-frequency domain. Then, for the multi-dimensional feature extraction was prone to the problem of “dimension disaster”, the principles of FDR was introduced in data mining to determine the classification ability of each individual feature, and the cross correlation coefficient was adopted to solve the problem that dealing with individual feature. Neglected the interrelationship between the features, a new feature selection algorithm was constructed. Finally, the eigenvectors were used for training and recognizing of SVM model. The experimental results showed the fault diagnosis system was valid and robust.

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Discrimination-Based Feature Selection for Multinomial Naïve Bayes Text Classification

Zhu

Wang

Xi-Juan

2006

Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead

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Speech recognition using weighted HMM and subspace projection approaches

Cited by 43 publications

References 30 publications

Study of Data Fusion Method for Fault Diagnosis Based on FDR Feature Selection Algorithm and HMM/SVM Model

Study of Data Fusion Method for Fault Diagnosis Based on FDR Feature Selection Algorithm and HMM/SVM Model

Fault Diagnosis of Rotating Machinery Based on FDR Feature Selection Algorithm and SVM

Discrimination-Based Feature Selection for Multinomial Naïve Bayes Text Classification

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