Lipreading

32 papers with code • 7 benchmarks • 6 datasets

Lipreading is a process of extracting speech by watching lip movements of a speaker in the absence of sound. Humans lipread all the time without even noticing. It is a big part in communication albeit not as dominant as audio. It is a very helpful skill to learn especially for those who are hard of hearing.

Deep Lipreading is the process of extracting speech from a video of a silent talking face using deep neural networks. It is also known by few other names: Visual Speech Recognition (VSR), Machine Lipreading, Automatic Lipreading etc.

The primary methodology involves two stages: i) Extracting visual and temporal features from a sequence of image frames from a silent talking video ii) Processing the sequence of features into units of speech e.g. characters, words, phrases etc. We can find several implementations of this methodology either done in two separate stages or trained end-to-end in one go.

Libraries

Use these libraries to find Lipreading models and implementations

Most implemented papers

LipNet: End-to-End Sentence-level Lipreading

rizkiarm/LipNet 5 Nov 2016

Lipreading is the task of decoding text from the movement of a speaker's mouth.

Combining Residual Networks with LSTMs for Lipreading

tstafylakis/Lipreading-ResNet 12 Mar 2017

We propose an end-to-end deep learning architecture for word-level visual speech recognition.

Deep Audio-Visual Speech Recognition

lordmartian/deep_avsr 6 Sep 2018

The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio.

End-to-end Audio-visual Speech Recognition with Conformers

zziz/pwc 12 Feb 2021

In this work, we present a hybrid CTC/Attention model based on a ResNet-18 and Convolution-augmented transformer (Conformer), that can be trained in an end-to-end manner.

End-to-end Audiovisual Speech Recognition

mpc001/end-to-end-Lipreading IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018

In presence of high levels of noise, the end-to-end audiovisual model significantly outperforms both audio-only models.

LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild

Fengdalu/Lipreading-DenseNet3D 16 Oct 2018

It has shown a large variation in this benchmark in several aspects, including the number of samples in each class, video resolution, lighting conditions, and speakers' attributes such as pose, age, gender, and make-up.

Lipreading using Temporal Convolutional Networks

mpc001/Lipreading_using_Temporal_Convolutional_Networks 23 Jan 2020

We present results on the largest publicly-available datasets for isolated word recognition in English and Mandarin, LRW and LRW1000, respectively.

Discriminative Multi-modality Speech Recognition

JackSyu/Discriminative-Multi-modality-Speech-Recognition CVPR 2020

Vision is often used as a complementary modality for audio speech recognition (ASR), especially in the noisy environment where performance of solo audio modality significantly deteriorates.

Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction

facebookresearch/av_hubert ICLR 2022

The lip-reading WER is further reduced to 26. 9% when using all 433 hours of labeled data from LRS3 and combined with self-training.

Visual Speech Recognition for Multiple Languages in the Wild

mpc001/Visual_Speech_Recognition_for_Multiple_Languages 26 Feb 2022

However, these advances are usually due to the larger training sets rather than the model design.