Learning cross-lingual knowledge with multilingual BLSTM for emphasis detection with limited training data

Bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) has achieved state-of-the-art performance in many sequence processing problems given its capability in capturing contextual information. However, for languages with limited amount of training data, it is still difficult to obtain a high quality BLSTM model for emphasis detection, the aim of which is to recognize the emphasized speech segments from natural speech. To address this problem, in this paper, we propose a multilingual BLSTM (MTL-BLSTM) model where the hidden layers are shared across different languages while the softmax output layer is language-dependent. The MTL-BLSTM can learn cross-lingual knowledge and transfer this knowledge to both languages to improve the emphasis detection performance. Experimental results demonstrate our method can outperform the comparison methods over 2–15.6% and 2.9–15.4% on the English corpus and Mandarin corpus in terms of relative F1-measure, respectively.