Summary of How “real” Is Your Real-time Simultaneous Speech-to-text Translation System?, by Sara Papi et al.

Main: Computation and Language (cs.CL)
Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)

How “Real” is Your Real-Time Simultaneous Speech-to-Text Translation System?

by Sara Papi, Peter Polak, Ondřej Bojar, Dominik Macháček

First submitted to arxiv on: 24 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This research paper presents a comprehensive review of simultaneous speech-to-text translation (SimulST), which aims to translate spoken language into text in real-time. The study reveals that existing research has primarily focused on pre-segmented human speech, ignoring the complexities of unbounded speech and resulting in limited applicability to real-world scenarios. To address this gap, the authors propose a standardized terminology and taxonomy for SimulST systems, analyze community trends, and provide concrete recommendations for advancing the field.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Simultaneous speech-to-text translation (SimulST) is a technology that converts spoken language into text as it’s being spoken. Currently, most research on this topic focuses on pre-segmented human speech, which makes it hard to apply to real-life situations. This paper looks at the current state of SimulST and suggests ways to improve it.