Abstract
This paper presents the comparison of different acoustic adaptation methods in a multilingual speech recognition environment. Baseline multilingual acoustic models were generated using the tree based clustering with common phonetic broad classes. After the expert based port to a new language was performed, the influence of several adaptation methods on speech recognition performance was investigated. The target language adaptation subset contained 2% of complete speech database. The best adapted ported system had significant improvement in the speech recognition performance and its results were close to the results of pure reference monolingual system. The relationship between languages used in the mapping configuration remained unchanged after the adaptation. ...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Žgank, A., Imperl, B., Johansen, F.T., Kačič, Z., Horvat, B.: Crosslingual Speech Recognition with Multilingual Acoustic Models Based on Agglomerative and Tree-Based Triphone Clustering. In: Proc. Eurospeech 2001, Aalborg, Denmark (2001)
Žgank, A., Imperl, B., Johansen, F.T., Kačič, Z., Horvat, B.: Crosslingual Adaptation of Multilingual Triphone Acoustic Models. In: Proc. MSLP 2001, Aalborg, Denmark (2001)
Schultz, T.: Multilinguale Spracherkennung - Kombination akustischer Modelle zur Portierung auf neue Sprachen. PhD Thesis, University of Karlsruhe, Germany (2000)
Nieuwoudt, C.: Cross-language acoustic adaptation for automatic speech recognition. PhD Thesis, University of Pretoria, South Africa (2000)
Leggetter, C.J., Woodland, P.C.: Flexible Speaker Adaptation using Maximum Likelihood Linear Regression. In: Proc. ARPA Spoken Language Technology Workshop, Austin, USA (1995)
van den Heuvel, H., Boves, L., Moreno, A., Omologo, M., Richard, G., Sanders, E.: Annotation in the SpeechDat Projects. International Journal of Speech Technology 4(2), 127–143 (2001)
Johansen, F.T., Warakagoda, N., Lindberg, B., Lehtinen, G., Kačič, Z., Žgank, A., Elenius, K., Salvi, G.: The COST 249 SpeechDat Multilingual Reference Recogniser. In: Proc. LREC 2000, Athens, Greece (2000)
Young, S.: The HTK Book (for HTK version 3.1). Cambridge University (2001)
Lindberg, B., Johansen, F.T., Warakagoda, N., Lehtinen, G., Kačič, Z., Žgank, A., Elenius, K., Salvi, G.: A noise robust multilingual reference recogniser based on SpeechDat(II). In: Proc. ICSLP 2000, Beijing, China (2000)
Imperl, B., Kačič, Z., Horvat, B., Žgank, A.: Clustering of triphones using phoneme similarity estimation for the definition of a multilingual set of triphones. Speech Communication 39(3-4), 353–366 (2003)
IPA Homepage (2003), http://www2.arts.gla.ac.uk/IPA/ipa.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Žgank, A., Kačič, Z., Horvat, B. (2003). Comparison of Acoustic Adaptation Methods in Multilingual Speech Recognition Environment. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2003. Lecture Notes in Computer Science(), vol 2807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39398-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-540-39398-6_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20024-6
Online ISBN: 978-3-540-39398-6
eBook Packages: Springer Book Archive