AI 기반 실시간 통역 보조 시스템 개발을 위한 예비 연구: 원문의 숫자 정보 제시를 중심으로
Prototype Development for an AI-based CAI Tool: Technological Support for Interpretation of Numbers
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The technological advances in natural language processing is ushering in a new era of augmented interpreting where human interpreters perform their tasks more easily and better with the support of technology. Against this backdrop, the current study is aimed at developing and presenting the prototype of an AI-based computer-assisted interpreting (CAI) tool to support interpreters in processing numbers in real time by the automated recognition and extraction of number information from the source speech. The prototype system was built by using Google Speech-to-Text API and spaCy for automatic speech recognition (ASR) and named entity recognition (NER), respectively, as main components. During a pilot run, the system performed with the precision of 0.82 and recall of 0.90. The prototype system was configured so that its output was presented in three different layouts: only numbers presented in a dialogue box; sentences with number visualization; and real-time transcription followed immediately by fully visualized sentences. It was expected that presenting the full sentences with number visualization would be the optimal option for interpreters due to the avthe context and easy location of numbers in a given speech segment.
Abstract
The technological advances in natural language processing is ushering in a new era of augmented interpreting where human interpreters perform their tasks more easily and better with the support of technology. Against this backdrop, the current study is aimed at developing and presenting the prototype of an AI-based computer-assisted interpreting (CAI) tool to support interpreters in processing numbers in real time by the automated recognition and extraction of number information from the source speech. The prototype system was built by using Google Speech-to-Text API and spaCy for automatic speech recognition (ASR) and named entity recognition (NER), respectively, as main components. During a pilot run, the system performed with the precision of 0.82 and recall of 0.90. The prototype system was configured so that its output was presented in three different layouts: only numbers presented in a dialogue box; sentences with number visualization; and real-time transcription followed immediately by fully visualized sentences. It was expected that presenting the full sentences with number visualization would be the optimal option for interpreters due to the avthe context and easy location of numbers in a given speech segment.
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