28,569 research outputs found
Lip Reading Sentences in the Wild
The goal of this work is to recognise phrases and sentences being spoken by a
talking face, with or without the audio. Unlike previous works that have
focussed on recognising a limited number of words or phrases, we tackle lip
reading as an open-world problem - unconstrained natural language sentences,
and in the wild videos.
Our key contributions are: (1) a 'Watch, Listen, Attend and Spell' (WLAS)
network that learns to transcribe videos of mouth motion to characters; (2) a
curriculum learning strategy to accelerate training and to reduce overfitting;
(3) a 'Lip Reading Sentences' (LRS) dataset for visual speech recognition,
consisting of over 100,000 natural sentences from British television.
The WLAS model trained on the LRS dataset surpasses the performance of all
previous work on standard lip reading benchmark datasets, often by a
significant margin. This lip reading performance beats a professional lip
reader on videos from BBC television, and we also demonstrate that visual
information helps to improve speech recognition performance even when the audio
is available
You said that?
We present a method for generating a video of a talking face. The method
takes as inputs: (i) still images of the target face, and (ii) an audio speech
segment; and outputs a video of the target face lip synched with the audio. The
method runs in real time and is applicable to faces and audio not seen at
training time.
To achieve this we propose an encoder-decoder CNN model that uses a joint
embedding of the face and audio to generate synthesised talking face video
frames. The model is trained on tens of hours of unlabelled videos.
We also show results of re-dubbing videos using speech from a different
person.Comment: https://youtu.be/LeufDSb15Kc British Machine Vision Conference
(BMVC), 201
- …