Abstract
In this work we present an approach to adapt speaker-independent recognizers to a new acoustical environment. The recognizers were trained with data which were recorded using a close-talking microphone. These recognizers are to be evaluated with distant-talking microphone data. The adaptation set was recorded with the same type of microphone. In order to keep the speaker-independency this set includes 33 speakers. The adaptation itself is done using maximum a posteriori (MAP) and maximum likelihood linear regression adaptation (MLLR) in combination with the Baum-Welch algorithm. Furthermore the close-talking training data were artificially reverberated to reduce the mismatch between training and test data. In this manner the performance could be increased from 9.9 % WA to 40.0 % WA in speaker-open conditions. If further speaker-dependent adaptation is applied this rate is increased up to 54.9 % WA.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gales, M., Pye, D., Woodland, P.: Variance compensation within the MLLR framework for robust speech recognition and speaker adaptation. In: Proc. ICSLP 1996, Philadelphia, USA, vol. 3, pp. 1832–1835 (1996)
Bocchieri, E., Riley, M., Saraclar, M.: Methods for task adaptation of acoustic models with limited transcribed in-domain data. In: Proc. ICSLP 2004, Jeju Island, Korea, pp. 326–329 (2004)
Batliner, A., Hacker, C., Steidl, S., Nöth, E.: You stupid tin box - children interacting with the AIBO robot: A cross-linguistic emotional speech corpus. In: Proc. of the 4th International Conference of Language Resources and Evaluation 2004, Lisbon, Portugal, pp. 171–174 (2004)
Wahlster, W.: Verbmobil: Foundations of Speech-to-Speech Translation. Springer, New York (2000)
Haderlein, T., Nöth, E., Herbordt, W., Kellermann, W., Niemann, H.: Using Artificially Reverberated Training Data in Distant-Talking ASR. In: Matoušek, V., Mautner, P., Pavelka, T. (eds.) TSD 2005. LNCS (LNAI), vol. 3658, pp. 226–233. Springer, Heidelberg (2005)
Sony, AIBO Europe – Official Website (2005), http://ww.aibo-europe.com
Stemmer, G.: Modeling Variability in Speech Recognition, Ph.D. thesis, Chair for Pattern Recognition, University of Erlangen-Nuremberg, Germany (2005)
Maier, A., Hacker, C., Nöth, E., Niemann, H.: Robust parallel speech recognition in multiple energy bands. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 133–140. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maier, A., Haderlein, T., Nöth, E. (2006). Environmental Adaptation with a Small Data Set of the Target Domain. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2006. Lecture Notes in Computer Science(), vol 4188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11846406_54
Download citation
DOI: https://doi.org/10.1007/11846406_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39090-9
Online ISBN: 978-3-540-39091-6
eBook Packages: Computer ScienceComputer Science (R0)