Adaptive Gaussian mixture model tuning with locality property on speaker identification
碩士 === 國立交通大學 === 資訊工程系 === 91 === This thesis discuss the Gaussian Mixture Models learning and use it on speaker identification. In pass research,it has the good result with using GMM on speaker identification,and someone maybe debate with the initial numbers of GMM...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2003
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| Online Access: | http://ndltd.ncl.edu.tw/handle/72440822809593377207 |
| Summary: | 碩士 === 國立交通大學 === 資訊工程系 === 91 === This thesis discuss the Gaussian Mixture Models learning and use it on speaker identification.
In pass research,it has the good result with using GMM on speaker identification,and someone maybe
debate with the initial numbers of GMM components.
In this thesis,we take one thinking SLUG(Supervised Learning and Unsupervised Growing) to train our GMM
models,and imporve our tuning step to just tuning a single cluster.
finally,we use the TCC-300 microphone speech database to be our experiment data to prove our theory.
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