Automatic Speech Recognition for Low-Resource Languages: The Thuee Systems for the IARPA Openasr20 Evaluation

The paper introduces our Automatic Speech Recognition (ASR) systems for the IARPA Open Automatic Speech Recognition Challenge (OpenASR20) as well as some post explorations with speech pre-training. We compete in the Constrained training condition for the 10 languages as team THUEE, under which the only speech data permissible for training of each language is a 10-hour corpus. We adopt the hybrid NN-HMM acoustic model and an N-gram Language Model (LM) to construct our basic ASR systems. The acoustic model is proposed as CNN-TDNNF-A, which combines Convolution Neural Network (CNN), Factored Time Delay Neural Network (TDNN-F) and self-attention mechanism. As for low-resource condition, we apply speed and volume perturbation, SpecAugment and reverberation for data enhancement as well as data clean-up to filter interference information. A series of pre-and-post-processing procedures for the evaluation set, such as Speech Activity Detection (SAD), system fusion and results filtering are carried out to obtain the final results. Furthermore, we exploit wav2vec 2.0 pre-trained model to obtain more effective speech representations for the hybrid system with a series of explorations, which brings about evident improvements.