Exploring data augmentation methods in reverberant human-robot voice communication

Abstract

Collecting training data is not an easy task especially in situation involving robots that require tremendous physical effort. The ability to augment data through synthetic means is a convenient tool to solve this problem. Therefore it is important to evaluate the extent of the usefulness of augmented data. In this paper, we will explore data augmentation schemes in reverberant environment and investigate a method to effectively select data. We experiment in a real reverberant environment condition and investigate both the traditional automatic speech recognition (ASR) system based on gaussian mixture model-hidden markov model (GMM-HMM) and the most current system based on Deep Neural Networks (i.e, HMM-DNN). Our results show that the combination of data augmentation and data selection, further improves system performance. In our experiments, we used real test data in a reverberant hands-free human-robot communication scenario.