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We study the performance of support vector machine based classifiers in acoustic model combination for recognition of context dependent sub word units of speech in multiple languages. In acoustic model combination, the data for similar sub word units across languages are shared to train acoustic models for multilingual speech. Sharing of data across languages leads to an increase in the number of training examples for a subword unit common to the languages. It may also lead to increase in the variability of the data for a subword unit. In This work, we study the effect of data sharing on the classification accuracy and complexity of acoustic models built using support vector machines. We compare the performance of multilingual acoustic models with that of monolingual acoustic models in the recognition of a large number of consonant-vowel units in the broadcast news corpus of three Indian languages.