LipNet

This project was basically started by Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, Nando de Freitas Oxford University in collaboration with google deep-minds in 2016.Lip-reading is the task of decoding text from the movement of a speaker’s mouth. Traditional approaches separated the problem into two stages: designing or learning visual features, and prediction. More recent deep lip-reading approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman, 2016a). However, existing work on models trained end-to-end perform only word classification, rather than sentence-level sequence prediction. Studies have shown that human lip-reading performance increases for longer words (Easton & Basala, 1982), indicating the importance of features capturing temporal context in an ambiguous communication channel. Motivated by this observation, we present our model LipSync, that maps a variable-length sequence of video frames to text, making use Deep neural networks, classification loss, trained entirely end-to-end. To the best of our knowledge, LipNet by Oxford University was the first end-to-end sentence-level lip-reading model that simultaneously learns spatiotemporal visual features and a sequence model. On the GRID audio-visual sentence corpus, LipNet achieves 95.2% accuracy in sentence-level, overlapped speaker split task, outperforming experienced human lip-readers and the previous 86.4% word-level state-of-the-art accuracy (Gergen et al., 2016).

Dataset:

MIRACL-VC1 Dataset - https://sites.google.com/site/achrafbenhamadou/-datasets/miracl-vc1It has the following labels:

Problem Statement:

Input : A Video file of a person speaking some word or phrase.
Output : The predicted word or phrase the person was speaking.

Technologies and frameworks :

- Tensorflow1.2.1
- Keras
- Opencv3
- python 3.5

Use case:

• Help in understanding what the speaker is speaking when there is noise in the background (like when travelling in a vehicle or watching a video in a very noise gatherings).
• Help for the deaf people to understand what the other person is speaking. If we can integrate this project to an IOT device, then this can be a device that can be sent to the production line to be a very big help for the people with disabled hearing.
• A tool that can be used by the intelligence agencies for spying purpose.

Reference :

• Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, Nando de Freitas, “ LipNet: End-To-End Sentence-Level Lip-reading”on 2016. Read link
• Amit Garg, Jonathan Noyola, Sameep Bagadia, 2017 “Lip reading using CNN and and LSTM ” on 2017 Read link
• Andrew Owens, Phillip Isola, Josh McDermott, Antonio Torralba, Edward H. Adelson, William T. Freeman, “Visually Indicated Sounds” on 2016 Read link
• Joon Son Chung, Andrew Senior, Oriol Vinyals, Andrew Zisserman, “Lip Reading in Wild” on 2016 Read link
• Joon Son Chung and Andrew Zisserman , “ Out of time: automated lip sync in the wild”, on 2016 Read link