Discriminative Product-of-Expert acoustic mapping for cross-lingual phone recognition

This paper presents a product-of-expert framework to perform probabilistic acoustic mapping for cross-lingual phone recognition. Under this framework, the posterior probabilities of the target HMM states are modelled as the weighted product of experts, where the experts or their weights are modelled as functions of the posterior probabilities of the source HMM states generated by a foreign phone recogniser. Careful choice of these functions leads to the product-of-posterior and posterior weighted product-of-expert models, which can be conveniently represented as 2-layer and 3-layer feed-forward neural networks respectively. Therefore, the commonly used error back-propagation method can be used to discriminatively train the model parameters. Experimental results are presented on the NTIMIT database using the Czech, Hungarian and Russian hybrid NN/HMM recognisers as the foreign phone recognisers to recognise English phones. With only about 15.6 minutes of training data, the best acoustic mapping model achieved 46.00% phone error rate, which is not far behind the 43.55% performance of the NN/HMM system trained directly on the full 3.31 hours of data.