Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis

Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise. In this work, we explore the task of lip to speech synthesis, i.e., learning to generate natural speech given only the lip movements of a speaker. Acknowledging t...

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Bibliographic Details
Main Authors: Prajwal, K R, Mukhopadhyay, Rudrabha, Namboodiri, Vinay P., Jawahar, C.V.
Format: Conference Proceeding
Language:English
Subjects:
Face
Lips
Natural languages
Speech synthesis
Task analysis
Videos
Vocabulary
Online Access:Request full text
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Description
Summary:Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise. In this work, we explore the task of lip to speech synthesis, i.e., learning to generate natural speech given only the lip movements of a speaker. Acknowledging the importance of contextual and speaker-specific cues for accurate lip-reading, we take a different path from existing works. We focus on learning accurate lip sequences to speech mappings for individual speakers in unconstrained, large vocabulary settings. To this end, we collect and release a large-scale benchmark dataset, the first of its kind, specifically to train and evaluate the single-speaker lip to speech task in natural settings. We propose a novel approach with key design choices to achieve accurate, natural lip to speech synthesis in such unconstrained scenarios for the first time. Extensive evaluation using quantitative, qualitative metrics and human evaluation shows that our method is four times more intelligible than previous works in this space.
ISSN:2575-7075
DOI:10.1109/CVPR42600.2020.01381