Cross-lingual acoustic modeling for Indian languages based on Subspace Gaussian Mixture Models

Cross-lingual acoustic modeling using Subspace Gaussian Mixture Model for low-resource languages of Indian origin is investigated. Building acoustic model for a low-resource language with limited vocabulary by leveraging resources from another language with comparatively larger resources was focused upon. Experiments were done on Bengali and Tamil corpus from MANDI database, with Tamil having greater resources than Bengali. We observed that the word accuracy of cross-lingual acoustic model of Bengali was approximately 2.5% above it's CDHMM model and gave equivalent performance as it's monolingual SGMM model.

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