Evaluation of Command and Speaker Recognition on Vietnamese Voice Dataset to Enhance Security

This paper presents our work in building a Vietnamese dataset for command and speaker recognition problems. We built a website that allows users of mobile devices or personal computers to be able to provide their voice samples easily. We collected more than fifteen thousand utterances of nineteen Vietnamese commands from more than two hundred volunteers. The commands are primarily used for applications on edge devices that interact with users via voice. Models of command recognition are often much smaller than those of speech recognition and, thus, are preferable on edge devices. We then fine-tuned and evaluated recent methods of recognizing voice commands and verifying speakers with our dataset. The selected methods rely on a moderately deep learning model that tries to vectorize input speech signals represented by their spectrograms and performs classification on the vectors with a multilayer perceptron network. The experimental results showed that the models fit very well on the dataset and are promising for applying to command and speaker verification in Vietnamese. The fine-tuned model of Vietnamese command recognition achieved an F1-score of 99%. The fine-tuned model of Vietnamese speaker verification had equal error rates of 4.49% and 2.27% with text-independent and text-dependent test sets respectively. Our solution can be applied to enhance security for smart interaction systems that need to authenticate users for critical functionalities.