Improving the Accuracy of Speech Recognition Models for Non-Native English Speakers using Bag-of-Words and Deep Neural Networks

摘要

This letter presents a novel error correction module using a Bag-of-Words model and deep neural networks to improve the accuracy of cloud-based speech-to-text services on recognition tasks of non-native speakers with foreign accents. The Bag-of-Words model transforms text into input vectors for the deep neural network, which is trained using typical sentences in the curriculum for elementary schools in Vietnam and the Google Speech-to-Text data for those sentences. The trained network is then used for real-time error correction on a humanoid robot and yields 18% better accuracy than Google Speech-to-Text.