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LRS3-TED: a large-scale dataset for visual speech recognition

Triantafyllos Afouras, Joon Son Chung, Andrew Zisserman

TL;DR

This paper introduces a new multi-modal dataset for visual and audio-visual speech recognition that includes face tracks from over 400 hours of TED and TEDx videos, along with the corresponding subtitles and word alignment boundaries.

Abstract

This paper introduces a new multi-modal dataset for visual and audio-visual speech recognition. It includes face tracks from over 400 hours of TED and TEDx videos, along with the corresponding subtitles and word alignment boundaries. The new dataset is substantially larger in scale compared to other public datasets that are available for general research.

LRS3-TED: a large-scale dataset for visual speech recognition

TL;DR

This paper introduces a new multi-modal dataset for visual and audio-visual speech recognition that includes face tracks from over 400 hours of TED and TEDx videos, along with the corresponding subtitles and word alignment boundaries.

Abstract

This paper introduces a new multi-modal dataset for visual and audio-visual speech recognition. It includes face tracks from over 400 hours of TED and TEDx videos, along with the corresponding subtitles and word alignment boundaries. The new dataset is substantially larger in scale compared to other public datasets that are available for general research.