中国科学院自动化研究所机构知识库

This paper proposes to learn language-invariant bottleneck features from an adversarial end-to-end acoustic model for low resource languages. The multilingual end-to-end model is trained with a connectionist temporal classification loss function. The model has shared and private layers. The shared layers are the hidden layers utilized to learn universal features for all the languages. The private layers are the language-dependent layers used to capture language-specific features. Attention based adversarial end-to-end language identification is used to capture enough language information. Furthermore, orthogonality constraints are used to make private and shared features dissimilar. Experiments are conducted on IARPA Babel datasets. The results show that the target model trained with the proposed language-invariant bottleneck features outperforms the target model trained with the conventional multilingual bottleneck features by up to 9.7% relative word error rate reduction.