One aspect of the embodiments discloses a model generation device for pattern recognition, a pattern recognition apparatus and methods thereof. A mixture-level variance sharing step generates a mixture-level variance sharing structure of a first model by using a second model. A first model generation step generates the first model with the variance sharing structure by using training data of the first model, wherein in the variance sharing structure, mixture components in respective states have the same shared variances in the same order. The embodiment can at least provide better model parameter estimation so as to provide better recognition performance in the case of limited training data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A model generation method for pattern recognition, comprising: a mixture-level variance sharing step for generating a mixture-level variance sharing structure of a first model by using a second model; and a first model generation step for generating the first model with the variance sharing structure by using training data of the first model, wherein in the variance sharing structure, mixture components in respective states have the same shared variances in the same order. 2. The model generation method according to claim 1, wherein the mixture-level variance sharing step further comprises the following steps: a variance sharing rule design step for designing a variance sharing rule by using the second model, the variance sharing rule specifying mixture components to be sharing variances among respective states; a shared variance generation step for generating shared variances based on the variance sharing rule; a mixture component reordering step for reordering mixture components in each state of the second model based on the generated shared variances so that the shared variances of the mixture components in respective states of the second model are in the same order; and a shared variance copying rule design step for designing a shared variance copying rule to generate the variance sharing structure by using the shared variances of the reordered mixture components. 3. The model generation method according to claim 2, wherein the variance sharing rule design step further comprises the following steps: selecting one reference state from the second model; and selecting a mixture component from the selected reference state one by one as a reference mixture component, and generating a nearest mixture component sequence for each selected reference mixture component, until all mixture components in the selected reference state have been selected, wherein respective mixture components in each nearest mixture component sequence are from respective states of the second model respectively, have the nearest distances among each others, and will have a shared variance. 4. The model generation method according to claim 3, wherein the step of generating a nearest mixture component sequence for each selected reference mixture component further comprises the following step: for the selected reference mixture component, selecting, from remaining states of the second model other than the selected reference state, a remaining state one by one, and obtaining one nearest mixture component for each selected remaining state, until all remaining states have been selected. 5. The model generation method according to claim 4, wherein the step of obtaining one nearest mixture component for each selected remaining state further comprises the following steps: for the selected remaining state, generating one mixture component based on at least a mixture component related to the selected reference mixture component, the at least mixture component related to the selected reference mixture component comprising the selected reference mixture component and all current nearest mixture components thereof; selecting a mixture component from the selected remaining state one by one, and measuring, for each selected mixture component, the distance between it and the generated one mixture component, until all mixture components in the selected remaining state have been selected; and comparing the measured distances and obtaining the mixture component with the smallest distance as the nearest mixture component. 6. The model generation method according to claim 4, wherein the variance sharing rule design step employs a constrained push-pop method, the variance sharing rule design step further comprises, before the step of selecting one reference state from the second model, the following step: initializing a push array and a pop array, the push array and the pop array being used for recording selected mixture components and unselected mixture components in each state of the second model respectively, the initialized push array being empty, and all mixture components in all states of the second model being recorded in the initialized pop array; the step of generating a nearest mixture component sequence for each selected reference mixture component further comprises, before the step of selecting, from remaining states of the second model other than the selected reference state, a remaining state one by one, the following step: moving the selected reference mixture component from the pop array to the push array; and the step of generating a nearest mixture component sequence for each selected reference mixture component further comprises, after the step of obtaining the one nearest mixture component for each selected remaining state, the following step: moving the obtained one nearest mixture component from the pop array to the push array. 7. The model generation method according to claim 3, wherein the shared variance generation step further comprises: obtaining respective nearest mixture component sequences; generating one mixture component by using each nearest mixture component sequence; and obtaining one shared variance by using the variance of each generated mixture component. 8. The model generation method according to claim 7, wherein the step of generating one mixture component by using each nearest mixture component sequence further comprises: generating the one mixture component by merging respective mixture components in each nearest mixture component sequence; or generating the one mixture component by obtaining a representative mixture component in each nearest mixture component sequence. 9. The model generation method according to claim 2, wherein the mixture component reordering step reorders the mixture components in each state of the second model based on the order of the generated shared variances. 10. The model generation method according to claim 2, wherein the shared variance copying rule design step further comprises: obtaining a starting position of the shared variances of the reordered mixture components; and repeatedly copying the shared variances of the reordered mixture components one by one to respective mixture components in each state of the first model, until all mixture components in each state of the first model have copied shared variances. 11. The model generation method according to claim 1, wherein the second model is generated off-line by using training data of the second model; the mixture-level variance sharing step is performed off-line; and the first model generation step is performed on-line. 12. The model generation method according to claim 1, wherein the second model is at least one of a universal background model and a background model. 13. The model generation method according to claim 12, wherein the second model is a Hidden Markov Model or a Gaussian Mixture Model. 14. The model generation method according to claim 3, wherein the distance is at least one of a Bhattacharyya distance and a symmetric Kullback-Leibler distance. 15. The model generation method according to claim 7, wherein the step of generating one mixture component by using each nearest mixture component sequence uses one of the following information: variance information of the nearest mixture component sequence; variance information and mean information of the nearest mixture component sequence; and variance information, mean information and mixture weight information of the nearest mixture component sequence. 16. A pattern recognition method, comprising the following steps: a feature extraction step for extracting features by using test data; and a pattern recognition step for performing pattern recognition on the extracted features by using the first model generated by the model generation method according to claim 1. 17. A model generation device for pattern recognition, comprising: a mixture-level variance sharing unit for generating a mixture-level variance sharing structure of a first model by using a second model; and a first model generation unit for generating the first model with the variance sharing structure by using training data of the first model, wherein in the variance sharing structure, mixture components in respective states have the same shared variances in the same order. 18. The model generation device according to claim 17, wherein the mixture-level variance sharing unit further comprises the following units: a variance sharing rule design unit for designing a variance sharing rule by using the second model, the variance sharing rule specifying mixture components to be sharing variances among respective states; a shared variance generation unit for generating shared variances based on the variance sharing rule; a mixture component reordering unit for reordering mixture components in each state of the second model based on the generated shared variances so that the shared variances of the mixture components in respective states of the second model are in the same order; and a shared variance copying rule design unit for designing a shared variance copying rule to generate the variance sharing structure by using the shared variances of the reordered mixture components. 19. The model generation device according to claim 18, wherein the variance sharing rule design unit further comprises the following units: a unit for selecting one reference state from the second model; and a unit for selecting a mixture component from the selected reference state one by one as a reference mixture component, and generating a nearest mixture component sequence for each selected reference mixture component, until all mixture components in the selected reference state have been selected, wherein respective mixture components in each nearest mixture component sequence are from respective states of the second model respectively, have the nearest distances among each others, and will have a shared variance. 20. The model generation device according to claim 19, wherein the unit of generating a nearest mixture component sequence for each selected reference mixture component further comprises the following unit: for the selected reference mixture component, a unit for selecting, from remaining states of the second model other than the selected reference state, a remaining state one by one, and obtaining one nearest mixture component for each selected remaining state, until all remaining states have been selected. 21. The model generation device according to claim 20, wherein the unit of obtaining one nearest mixture component for each selected remaining state further comprises the following units: a unit for generating, for the selected remaining state, one mixture component based on at least a mixture component related to the selected reference mixture component, the at least mixture component related to the selected reference mixture component comprising the selected reference mixture component and all current nearest mixture components thereof; a unit for selecting a mixture component from the selected remaining state one by one, and measuring, for each selected mixture component, the distance between it and the generated one mixture component, until all mixture components in the selected remaining state have been selected; and a unit for comparing the measured distances and obtaining the mixture component with the smallest distance as the nearest mixture component. 22. The model generation device according to claim 20, wherein the variance sharing rule design unit employs a constrained push-pop method, the variance sharing rule design unit further comprises a unit for initializing a push array and a pop array before selecting one reference state from the second model, the push array and the pop array being used for recording selected mixture components and unselected mixture components in each state of the second model respectively, the initialized push array being empty, and all mixture components in all states of the second model being recorded in the initialized pop array; the unit of generating a nearest mixture component sequence for each selected reference mixture component further comprises a unit for moving the selected reference mixture component from the pop array to the push array before selecting, from remaining states of the second model other than the selected reference state, a remaining state one by one; and the unit of generating a nearest mixture component sequence for each selected reference mixture component further comprises a unit for moving the obtained one nearest mixture component from the pop array to the push array after obtaining the one nearest mixture component for each selected remaining state. 23. The model generation device according to claim 19, wherein the shared variance generation unit further comprises the following units: a unit for obtaining respective nearest mixture component sequences; a unit for generating one mixture component by using each nearest mixture component sequence; and a unit for obtaining one shared variance by using the variance of each generated mixture component. 24. The model generation device according to claim 23, wherein the unit of generating one mixture component by using each nearest mixture component sequence further comprises the following unit: a unit for generating the one mixture component by merging respective mixture components in each nearest mixture component sequence; or a unit for generating the one mixture component by obtaining a representative mixture component in each nearest mixture component sequence. 25. The model generation device according to claim 18, wherein the mixture component reordering unit reorders the mixture components in each state of the second model based on the order of the generated shared variances. 26. The model generation device according to claim 18, wherein the shared variance copying rule design unit further comprises the following units: a unit for obtaining a starting position of the shared variances of the reordered mixture components; and a unit for repeatedly copying the shared variances of the reordered mixture components one by one to respective mixture components in each state of the first model, until all mixture components in each state of the first model have copied shared variances. 27. The model generation device according to claim 17, wherein the second model is generated off-line by using training data of the second model; the mixture-level variance sharing unit generates the mixture-level variance sharing structure of the first model off-line; and the first model generation unit generates the first model with the variance sharing structure on-line. 28. The model generation device according to claim 17, wherein the second model is at least one of a universal background model and a background model. 29. The model generation device according to claim 28, wherein the second model is a Hidden Markov Model or a Gaussian Mixture Model. 30. The model generation device according to claim 19, wherein the distance is at least one of a Bhattacharyya distance and a symmetric Kullback-Leibler distance. 31. The model generation device according to claim 23, wherein the unit of generating one mixture component by using each nearest mixture component sequence uses one of the following information: variance information of the nearest mixture component sequence; variance information and mean information of the nearest mixture component sequence; and variance information, mean information and mixture weight information of the nearest mixture component sequence. 32. A pattern recognition apparatus, comprising the following devices: a feature extraction device for extracting features by using test data; and a pattern recognition device for performing pattern recognition on the extracted features by using the first model generated by the model generation device according to claim 17.