An SVM Based Approach to Cross-Language Adaptation for Indian Languages

In this paper we present air 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 tire 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.