Context dependant phone mapping for cross-lingual acoustic modeling

This paper presents a novel method for acoustic modeling with limited training data. The idea is to leverage on a well-trained acoustic model of a source language. In this paper, a conventional HMM/GMM triphone acoustic model of the source language is used to derive likelihood scores for each feature vector of the target language. These scores are then mapped to triphones of the target language using neural networks. We conduct a case study where Malay is the source language while English (Aurora-4 task) is the target language. Experimental results on the Aurora-4 (clean test set) show that by using only 7, 16, and 55 minutes of English training data, we achieve 21.58%, 17.97%, and 12.93% word error rate, respectively. These results outperform the conventional HMM/GMM and hybrid systems significantly.