Convolutional recurrent neural network with attention for Vietnamese speech to text problem in the operating room. (3rd May 2021) - Dat, Trinh Tan; Dang, Le Tran Anh; Sang, Vu Ngoc Thanh; Thuy, Le Nhi Lam; Bao, Pham The
We introduce automatic Vietnamese speech recognition (ASR) system for converting Vietnamese speech to text on a real operating room ambient noise recorded during liver surgery. First, we propose applying a combination between convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) for investigating local speech feature learning, sequence modelling, and transcription for speech recognition. We also extend the CNN-LSTM framework with an attention mechanism to decode the frames into a sequence of words. The CNN, LSTM and attention models are combining into a unified architecture. In addition, we combine connectionist temporal classification (CTC) and attention's loss functions in training phase. The length of the output label sequence from CTC is applied to the attention-based decoder predictions to make the final label sequence. This process helps to decrease irregular alignments and make speedup of the label sequence estimation during training and inference, instead of only relying on the data-driven attention-based encoder-decoder for estimating the label sequence in long sentences. The proposed system is evaluated using a real operating room database. The results show that our method significantly enhances the performance of the ASR system. We find that our approach provides a 13.05% in WER and outperforms standard methods.