A data-driven phoneme mapping technique using interpolation vectors of phone-cluster adaptive training

One of the major problems in acoustic modeling for a low-resource language is data sparsity. In recent years, cross-lingual acoustic modeling techniques have been employed to overcome this problem. In this paper we propose multiple cross-lingual techniques to address the problem of data insufficiency. The first method, which we call as the cross-lingual phone-CAT, uses the principles of phone-cluster adaptive training (phone-CAT), where the parameters of context-dependent states are obtained by linear interpolation of monophone cluster models. The second method uses the interpolation vectors of phone-CAT, which is known to capture the phonetic context information, to map phonemes between two languages. Finally, the data-driven phoneme-mapping technique is incorporated into the cross-lingual phone-CAT, to obtain what we call as the phoneme-mapped cross-lingual phone-CAT. The proposed techniques are employed in acoustic modeling of three Indian languages namely Bengali, Hindi and Tamil. The phoneme-mapped cross-lingual phone-CAT gave relative improvements of 15.14% for Bengali, 16.4% for Hindi and 11.3% for Tamil over the conventional cross-lingual subspace Gaussian mixture model (SGMM) in low-resource scenario.