Katri Leino
Department of Signal Processing and Acoustics, Aalto University, Finland
Mikko Kurimo
Department of Signal Processing and Acoustics, Aalto University, Finland
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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, p. 65-69
Publicerad: 2017-05-08
ISBN: 978-91-7685-601-7
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
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.
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