TY - GEN
T1 - Subspace pursuit method for kernel-log-linear models
AU - Kubo, Yotaro
AU - Wiesler, Simon
AU - Schlueter, Ralf
AU - Ney, Hermann
AU - Watanabe, Shinji
AU - Nakamura, Atsushi
AU - Kobayashi, Tetsunori
PY - 2011
Y1 - 2011
N2 - This paper presents a novel method for reducing the dimensionality of kernel spaces. Recently, to maintain the convexity of training, log-linear models without mixtures have been used as emission probability density functions in hidden Markov models for automatic speech recognition. In that framework, nonlinearly-transformed high-dimensional features are used to achieve the nonlinear classification of the original observation vectors without using mixtures. In this paper, with the goal of using high-dimensional features in kernel spaces, the cutting plane subspace pursuit method proposed for support vector machines is generalized and applied to log-linear models. The experimental results show that the proposed method achieved an efficient approximation of the feature space by using a limited number of basis vectors.
AB - This paper presents a novel method for reducing the dimensionality of kernel spaces. Recently, to maintain the convexity of training, log-linear models without mixtures have been used as emission probability density functions in hidden Markov models for automatic speech recognition. In that framework, nonlinearly-transformed high-dimensional features are used to achieve the nonlinear classification of the original observation vectors without using mixtures. In this paper, with the goal of using high-dimensional features in kernel spaces, the cutting plane subspace pursuit method proposed for support vector machines is generalized and applied to log-linear models. The experimental results show that the proposed method achieved an efficient approximation of the feature space by using a limited number of basis vectors.
KW - Automatic speech recognition
KW - dimensionality reduction
KW - kernel method
KW - log-linear model
KW - subspace method
UR - http://www.scopus.com/inward/record.url?scp=80051616793&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80051616793&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2011.5947354
DO - 10.1109/ICASSP.2011.5947354
M3 - Conference contribution
AN - SCOPUS:80051616793
SN - 9781457705397
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4500
EP - 4503
BT - 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
T2 - 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Y2 - 22 May 2011 through 27 May 2011
ER -