自動口說評估於英語作為第二語言學習者的初步研究

A Preliminary Study on Automated Speaking Assessment of English as a Second Language

為順應國際化潮流，大學生對於國際交流以及英語授課所需之英文口說有越來越迫切的需求。本論文旨在發展自動化英語評測系統，並初探臺灣大學生之英語精熟程度。基於近期在臺灣所蒐集的口說語料，我們藉由一套自動語音辨識(Automatic Speech Recognition, ASR)系統，將語音轉寫成文字並擷取其中聲學特徵，最後使用機器學習模型來挑選適用的特徵以預測學生英語口說精熟度(English Speaking Proficiency)。經一系列所蒐集的臺灣大學生口說測驗語料的實驗和分析顯示，使用機器學習方法來進行自動英語口說能力分級，能較專家人工分級有更高的穩定性。

Due to the surge in global demand for English as a second language (ESL), developments of automated methods for grading speaking proficiency have gained considerable attention. This paper aims to present a computerized regime of grading the spontaneous spoken language for ESL learners. Based on the speech corpus of ESL learners recently collected in Taiwan, we first extract multi-view features (e.g., pronunciation, fluency, and prosody features) from either automatic speech recognition (ASR) transcription or audio signals. These extracted features are, in turn, fed into a tree-based classifier to produce a new set of indicative features as the input of the automated assessment system, viz. the grader. Finally, we use different machine learning models to predict ESL learners' respective speaking proficiency and map the result into the corresponding CEFR level. The experimental results and analysis conducted on the speech corpus of ESL learners in Taiwan show that our approach holds great potential for use in automated speaking assessment, meanwhile offering more reliable predictive results than the human experts.