SOTAVerified

Lip Reading

Lip Reading is a task to infer the speech content in a video by using only the visual information, especially the lip movements. It has many crucial applications in practice, such as assisting audio-based speech recognition, biometric authentication and aiding hearing-impaired people.

Source: Mutual Information Maximization for Effective Lip Reading

Papers

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Showing 101–125 of 153 papers

TitleStatusHype
Disentangling Homophemes in Lip Reading using Perplexity Analysis—0
Learn an Effective Lip Reading Model without PainsCode1
Lip-reading with Densely Connected Temporal Convolutional NetworksCode1
A Study on Lip Localization Techniques used for Lip reading from a Video—0
Seeing wake words: Audio-visual Keyword SpottingCode1
Learning Individual Speaking Styles for Accurate Lip to Speech SynthesisCode1
Synchronous Bidirectional Learning for Multilingual Lip ReadingCode0
Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision—0
Mutual Information Maximization for Effective Lip ReadingCode1
Deformation Flow Based Two-Stream Network for Lip ReadingCode1
Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading—0
Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech RecognitionCode1
Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition—0
Lipreading using Temporal Convolutional NetworksCode1
ASR is all you need: cross-modal distillation for lip reading—0
Hearing Lips: Improving Lip Reading by Distilling Speech RecognizersCode0
Towards Pose-invariant Lip-Reading—0
Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading—0
Multi-Grained Spatio-temporal Modeling for Lip-reading—0
A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading—0
Realistic Speech-Driven Facial Animation with GANs—0
MobiVSR: A Visual Speech Recognition Solution for Mobile Devices—0
Synthesising 3D Facial Motion from "In-the-Wild" Speech—0
Learning from Videos with Deep Convolutional LSTM Networks—0
An Empirical Analysis of Deep Audio-Visual Models for Speech Recognition—0
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All datasetsGRID corpus (mixed-speech)LRWTCD-TIMIT corpus (mixed-speech)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Lip2WavWER14.08—Unverified
#ModelMetricClaimedVerifiedStatus
1Lip2WavWER34.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Lip2WavWER31.26—Unverified