Language adaptive multilingual CTC speech recognition

Abstract

Recently, it has been demonstrated that speech recognition systems are able to achieve human parity. While much research is done for resource-rich languages like English, there exists a long tail of languages for which no speech recognition systems do yet exist. The major obstacle in building systems for new languages is the lack of available resources. In the past, several methods have been proposed to build systems in low-resource conditions by using data from additional source languages during training. While it has been shown that DNN/HMM hybrid setups trained in low-resource conditions benefit from additional data, we are proposing a similar technique using sequence based neural network acoustic models with Connectionist Temporal Classification (CTC) loss function. We demonstrate that setups with multilingual phone sets benefit from the addition of Language Feature Vectors (LFVs).