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Dictating translations with automatic speech recognition: Effects on translators' performance.

Lulu Wang¹, Sanjun Sun¹

¹School of English and International Studies, Beijing Foreign Studies University, Beijing, China.

Frontiers in Psychology

|February 6, 2023

Summary

Automatic speech recognition (ASR) for translation dictation reduces typing effort but does not save time or lower cognitive load. Text difficulty did not impact these translation performance factors.

Keywords:

automatic speech recognition cognitive effort input modality keylogging sight translation speech-to-text conversion translation process

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Area of Science:

Human-Computer Interaction
Translation Studies
Cognitive Psychology

Background:

Previous studies on automatic speech recognition (ASR) for translation productivity had small sample sizes.
These studies often overlooked translation quality and cognitive effort.
Technology's role in enhancing translator productivity warrants further investigation.

Purpose of the Study:

To investigate the impact of text input method (dictation vs. typing) on translator performance.
To assess effects on task duration, time allocation, editing, cognitive effort, and translation quality.
To determine if source-text difficulty influences these performance metrics.

Main Methods:

60 Chinese translation trainees were randomly assigned to dictation (ASR) or typing groups.
Participants completed two English-to-Chinese translations of varying difficulty.
Data collected via keylogging, subjective ratings, screen recording, and questionnaires.

Main Results:

ASR reduced participants' typing effort without compromising translation quality.
ASR did not lead to significant time savings or reduced cognitive effort.
Source-text difficulty did not demonstrably affect the measured performance factors.

Conclusions:

ASR technology can alleviate physical typing demands for translators.
Current ASR implementation does not optimize time efficiency or cognitive load in translation.
Further analysis of ASR revisions and user feedback can guide system optimization for translation workflows.