LipsID Using 3D Convolutional Neural Networks
This paper presents a proposition for a method inspired by iVectors for improvement of visual speech recognition in the similar way iVectors are used to improve the recognition rate of audio speech recognition. A neural network for feature extraction is presented with training parameters and evaluation. The network is trained as a classifier for a closed set of 64 speakers from the UWB-HSCAVC dataset and then the last softmax fully connected layer is removed to gain a feature vector of size 256. The network is provided with sequences of 15 frames and outputs the softmax classification to 64 classes. The training data consists of approximately 20000 sequences of grayscale images from the first 50 sentences that are common to every speaker. The network is then evaluated on the 60000 sequences created from 150 sentences from each speaker. The testing sentences are different for each speaker.
KeywordsVisual speech Neural network 3D convolution Deep features
This work was supported by the Ministry of Education of the Czech Republic, project No. LTARF18017. The work has been also supported by the grant of the University of West Bohemia, project No. SGS-2016-039. This work was supported by the Government of the Russian Federation (grant No. 08-08) and the Russian Foundation for Basic Research (project No. 18-07-01407) too. Moreover, access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.
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