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Visual recognition of speech using the lip movement is called Lip-reading.Recent developments in this nascent field uses different neural networks asfeature extractors which serve as input to a model which can map the temporalrelationship and classify. Though end to end sentence level Lip-reading is thecurrent trend, we proposed a new model which employs word level classificationand breaks the set benchmarks for standard datasets. In our model we useconvolutional autoencoders as feature extractors which are then fed to a Longshort-term memory model. We tested our proposed model on BBC's LRW dataset,MIRACL-VC1 and GRID dataset. Achieving a classification accuracy of 98% onMIRACL-VC1 as compared to 93.4% of the set benchmark (Rekik et al., 2014). OnBBC's LRW the proposed model performed better than the baseline model ofconvolutional neural networks and Long short-term memory model (Garg et al.,2016). Showing the features learned by the models we clearly indicate how theproposed model works better than the baseline model. The same model can also beextended for end to end sentence level classification.

Lip Reading Using Convolutional Auto Encoders as Feature Extractor