A preliminary study on cross-language knowledge transfer for low-resource Taiwanese Mandarin ASR

The deep neural networks (DNNs) are the state-of-the-art automatic speech recognition (ASR) technique nowadays. However, the key to success is that a large amount of speech data of the target language is required to well train DNNs. Unfortunately, there are only few small Taiwanese Mandarin Speech corpora available in Taiwan. Therefore, in this paper, two cross-language knowledge transfer approaches are evaluated for building a high performance Taiwanese Mandarin ASR including (1) a borrowed-hidden-layer and (2) a shared-hidden-layer method. Experimental results show that (1) the shared-hidden-layer method achieved the best performance and (2) the system is robustness to different speaker and phone.