Konferensartikel

Acoustic Model Compression with MAP adaptation

Katri Leino
Department of Signal Processing and Acoustics, Aalto University, Finland

Mikko Kurimo
Department of Signal Processing and Acoustics, Aalto University, Finland

Ladda ner artikel

Ingår i: Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden

Linköping Electronic Conference Proceedings 131:8, s. 65-69

NEALT Proceedings Series 29:8, s. 65-69

Visa mer +

Publicerad: 2017-05-08

ISBN: 978-91-7685-601-7

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

Speaker adaptation is an important step in optimization and personalization of the performance of automatic speech recognition (ASR) for individual users. While many applications target in rapid adaptation by various global transformations, slower adaptation to obtain a higher level of personalization would be useful for many active ASR users, especially for those whose speech is not recognized well. This paper studies the outcome of combinations of maximum a posterior (MAP) adaptation and compression of Gaussian mixture models. An important result that has not received much previous attention is how MAP adaptation can be utilized to radically decrease the size of the models as they get tuned to a particular speaker. This is particularly relevant for small personal devices which should provide accurate recognition in real-time despite a low memory, computation, and electricity consumption. With our method we are able to decrease the model complexity with MAP adaptation while increasing the accuracy.

Nyckelord

Inga nyckelord är tillgängliga

Referenser

Enrico Bocchieri and Brian Kan-Wing Mak. 2001. Subspace Distribution Clustering Hidden Markov Model. Speech and Audio Processing, IEEE Transactions on, 9(3):264–275.

David F. Crouse, Peter Willett, Krishna Pattipati, and Lennart Svensson. 2011. A look at Gaussian mixture reduction algorithms. In Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference.

Jean-Luc Gauvain and Chin-Hui Lee. 1994. Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. Speech and audio processing, ieee transactions on, 2(2):291–298.

Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al. 2012. Deep Neural Networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 29(6):82–97.

Teemu Hirsimaki, Janne Pylkkonen, and Mikko Kurimo. 2009. Importance of high-order n-gram models in morph-based speech recognition. Audio, Speech, and Language Processing, IEEE Transactions on, 17(4):724–732.

Xuedong Huang and Kai-Fu Lee. 1993. On speakerindependent, speaker-dependent, and speakeradaptive speech recognition. IEEE Transactions on Speech and Audio processing, 1(2):150–157.

Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, and Raj Foreword By-Reddy. 2001. Spoken language processing: A guide to theory, algorithm, and system development. Prentice Hall PTR.

Mei-Yuh Hwang and Xuedong Huang. 1993. Shareddistribution Hidden Markov Models for speech recognition. Speech and Audio Processing, IEEE Transactions on, 1(4):414–420.

Dorota J Iskra, Beate Grosskopf, Krzysztof Marasek, Henk van den Heuvel, Frank Diehl, and Andreas Kiessling. 2002. SPEECON-Speech databases for consumer devices: Database specification and validation. In LREC.

Christopher J Leggetter and Philip C Woodland. 1995. Maximum Likelihood Linear Regression for speaker adaptation of continuous density Hidden Markov Models. Computer Speech & Language, 9(2):171–185.

Harsh Vardhan Sharma and Mark Hasegawa-Johnson. 2010. State-transition interpolation and MAP adaptation for HMM-based dysarthric speech recognition. In Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies, pages 72–79. Association for Computational Linguistics.

Steve Young, Gunnar Evermann, Mark Gales, Thomas Hain, Dan Kershaw, Xunying Liu, Gareth Moore, Julian Odell, Dave Ollason, Dan Povey, et al. 1997. The HTK book, volume 2. Entropic Cambridge Research Laboratory Cambridge.

Citeringar i Crossref