Abstract
In this paper we present an evaluation of different approaches to cross-language adaptation for Indian languages. We also propose a method for cross-language adaptation of the SVM (support vector machine) based system. The proposed method gives approximately the same performance as pooling, with a reduction in the training time. The adaptation methods such as Bootstrap, MAP (Maximum A Posterior) and MLLR (Maximum Likelihood Linear Regression) have been used for cross-language adaptation in the HMM (hidden Markov model) based systems. We present a comparison of these adaptation techniques for three Indian languages, Tamil, Telugu and Hindi. The results show that the SVM based methods perform better than the HMM based methods when the 2-best and 5-best performance is considered.
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Vijaya Rama Raju, A., Chandra Sekhar, C. (2009). An SVM Based Approach to Cross-Language Adaptation for Indian Languages. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_48
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DOI: https://doi.org/10.1007/978-3-642-03040-6_48
Publisher Name: Springer, Berlin, Heidelberg
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