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Contents

Probabilistic bilinear transformation space-based joint maximum a posteriori adaptation / Hwa Jeon Song ; Yunkeun Lee ; Hyung Soon Kim 1

[요약] 1

I. Introduction 1

II. BIT-Based Speaker Adaptation 1

III. Probabilistic BIT 2

IV. Joint Probabilistic BIT-MAP Adaptation 3

V. Experiments and Results 3

VI. Conclusion 4

References 4

초록보기

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 bas

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참고문헌 (8건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Maximum Likelihood Training and Adaptation of Embedded Speech Recognizers for Mobile Environments 소장
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. 미소장
3 Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models 네이버 미소장
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. 미소장
5 Rapid speaker adaptation using probabilistic principal component analysis 네이버 미소장
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. 미소장
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. 미소장
8 Mixtures of probabilistic principal component analyzers. 네이버 미소장

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