论文信息 - Evaluating Automatic Speech Recognition in Translation

Evaluating Automatic Speech Recognition in Translation

We address and evaluate the challenges of utilizing Automatic Speech Recognition (ASR) to support the human translator. Audio transcription and translation are known to be far more time-consuming than text translation; at least 2 to 3 times longer. Furthermore, time to translate or transcribe audio is vastly dependent on audio quality, which can be impaired by background noise, overlapping voices, and other acoustic conditions. The purpose of this paper is to explore the integration of ASR in the translation workflow and evaluate the challenges of utilizing ASR to support the human translator. We present several case studies in different settings in order to evaluate the benefits of ASR. Time is the primary factor in this evaluation. We show that ASR might be effectively used to assist, but not replace, the human translator in essential ways.

Evelyne Tzoukermann | Corey Miller

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