Speaker Identification Using Discriminative Adapted HCRF models
碩士 === 元智大學 === 通訊工程學系 === 99 === This thesis presents the techniques of speaker identification using discriminative adapted hidden conditional random field (HCRF) models. A HCRF-based framework is adopted for the universal background model (UBM) and speaker models. We adapt the UBM to form a specif...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/93719537822517049731 |
| Summary: | 碩士 === 元智大學 === 通訊工程學系 === 99 === This thesis presents the techniques of speaker identification using discriminative adapted hidden conditional random field (HCRF) models. A HCRF-based framework is adopted for the universal background model (UBM) and speaker models. We adapt the UBM to form a specific speaker model using a model transformation-based technology in HCRF-based framework with the speaker’s enrollment speech. A novel training algorithm combining the discriminative training criterion with HCRF for speaker identification is also proposed. This work also adopted discriminative training technique to train GMM/UBM, HMM/UBM, and HCRF/UBM speaker models respectively; and the performances of speaker identification by the three speaker models with different amounts of training speech for testing speech were investigated. The experimental results indicate that the HCRF/UBM model consistently achieved the lowest error rate among the three models regardless of the length of the test and training speech and presence of noise.
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