Improving HMM/DNN in ASR of under-resourced languages using probabilistic sampling

In HMM/DNN automatic speech recognition (ASR) systems, the DNNs model the posterior probabilities for triphone states. However, triphone states are unevenly distributed. In this situation, the training algorithm tends to converge to a local optimum more related to states with rich data than states with poor data. Thus, the imbalance of the training data decreases the ASR performances, especially for under-resourced languages. To deal with this issue, we explore a resampling technique, called “probabilistic sampling”, which can be seen as a linear smoothing between the original sampling and the uniform sampling. The effectiveness of the probabilistic sampling has been studied in two under-resourced ASR experiments. With the probabilistic sampling, the first experiment got a 6.3% relative phone error rate (PER) reduction compared to the conventional DNN baseline; the second experiment used shared-hidden-layer multilingual DNN as the baseline, and obtained a 4.9% relative PER reduction.