Cross-language speech attribute detection and phone recognition for Tibetan using deep learning

Articulatory features (AFs) are viewed as the universal speech attributes for cross-language speech recognition. They are usually detected using a bank of multi-layer perceptrons (MLPs) in a supervised manner. In this paper, we propose to apply the deep learning method to detect AF-based speech attributes in a semi-supervised manner for cross-language speech recognition. The experimental results on Tibetan phone recognition showed that the deep learning method can detect the AF-based speech attributes more accurately and has higher phone recognition rates than MLPs.