Speech-Based L2 Call System for English Foreign Speakers

摘要

In this paper, we are presenting a language learning system which automatically evaluates English speech linguistically and grammatically. The system works by prompting the learner a question in his native language (text+figure) and waiting for his/her spoken response in English. Different types of features were extracted from the response to assess it in terms of language grammar and meaning errors. The universal sentence encoder was used to encode each sentence into 512-dimensional vector to represent the semantic of the response. Also, we propose a binary embedding approach to produce 438 binary features vectors from the student response. To assess the grammatical errors, different features were extracted using a grammar checker tool and part of speech analysis of the response. Finally, the best two DNN-based models have been fused together to enhance the system performance. The best result on the 2018 shared task test dataset is a D-score of 17.11.