An End to End Model for Vietnamese Speech Recognition
This paper presents an approach of End to End model based on Long Short Term Memory (LSTM) and Time Delay Deep Neural Network (TDNN) models for Vietnamese speech recognition. Two Vietnamese End to End architectures using Connectionist Temporal Classification (CTC) as the loss function are proposed. The paper also presents the method to construct phonesets based on Vietnamese characters or tonemes to produce the label sequence for any given transcription when applying CTC model. The experimental results showed that CTC based End To End models are competitive to traditional models with only 5% of WER worse for Vietnamese speech recognition (SR) but the advantage is no requirement of forced alignment for training acoustic models. In addition, it is similar to proposed studies using traditional models, the tone information and the toneme set are solutions to optimize the performance for Vietnamese SR.
2019 IEEE RIVF International Conference on Computing and Communication Technologies (RIVF)