期刊论文详细信息
ETRI Journal | |
Probabilistic Bilinear Transformation Space-Based Joint Maximum A Posteriori Adaptation | |
关键词: MAP; bilinear model; Speaker adaptation; | |
Others : 1186310 DOI : 10.4218/etrij.12.0212.0054 |
|
【 摘 要 】
This letter proposes a more advanced joint maximum a posteriori (MAP) adaptation using a prior model based on a probabilistic scheme utilizing the bilinear transformation (BIT) concept. The proposed method not only has scalable parameters but is also based on a single prior distribution without the heuristic parameters of the previous joint BIT-MAP method. Experiment results, irrespective of the amount of adaptation data, show that the proposed method leads to a consistent improvement over the previous method.
【 授权许可】
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
20150520124239332.pdf | 335KB | download |
【 参考文献 】
- [1]Y. Cho and D. Yook, “Maximum Likelihood Training and Adaptation of Embedded Speech Recognizers for Mobile Environments,” ETRI J., vol. 32, no. 1, Feb. 2010, pp. 160-162.
- [2]K.-T. Chen and H.-M. Wang, “Eigenspace-Based Maximum a posteriori Linear Regression for Rapid Speaker Adaptation,” Proc. ICASSP, 2000, p. 317-320.
- [3]C.J. Leggetter and P.C. Woodland, “Maximum Likelihood Linear Regression for Speaker Adaptation of Continuous Density Hidden Markov Models,” Computer Speech Language, vol. 9, no. 2, Apr. 1995, pp. 171-185.
- [4]J.-L. Gauvain and C.-H. Lee, “Maximum a posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains,” IEEE Trans. Speech Audio Process., vol. 2, no. 1, Apr. 1994, pp. 291-298.
- [5]D.K. Kim and N.S. Kim, “Rapid Speaker Adaptation Using Probabilistic Principal Component Analysis,” IEEE Signal Process. Lett., vol. 8, no. 6, June 2001, pp. 180-183.
- [6]O. Siohan, C. Chesta, and C.-H. Lee, “Joint Maximum a posteriori Adaptation of Transformation and HMM Parameters,” Proc. ICASSP, 2001, pp. 2945-2948.
- [7]H.J. Song, Y. Lee, and H.S. Kim, “Joint Bilinear Transformation Space Based Maximum a posteriori Linear Regression Adaptation Using Prior with Variance Function,” Proc. Interspeech, 2011, pp. 2577-2580.
- [8]M.E. Tipping and C.M. Bishop, “Mixtures of Probabilistic Principal Component Analyzers,” Neural Computation, vol. 11, no. 2, 1999, pp. 443-482.