abstract
several end-to-end deep learning approaches have been recently
presented which simultaneously extract visual features from the
input images and perform visual speech classification. however, research on jointly extracting audio and visual features
and performing classification is very limited. in this work, we
present an end-to-end audiovisual model based on bidirectional
long short-term memory (blstm) networks. to the best of
our knowledge, this is the first audiovisual fusion model which
simultaneously learns to extract features directly from the pixels
and spectrograms and perform classification of speech and nonlinguistic vocalisations. the model consists of multiple identical streams, one for each modality, which extract features directly from mouth regions and spectrograms. the temporal dynamics in each stream/modality are modeled by a blstm and
the fusion of multiple streams/modalities takes place via another
blstm. an absolute improvement of 1.9% in the mean f1 of 4
nonlingusitic vocalisations over audio-only classification is reported on the avic database. at the same time, the proposed
end-to-end audiovisual fusion system improves the state-of-theart performance on the avic database leading to a 9.7% absolute increase in the mean f1 measure. we also perform audiovisual speech recognition experiments on the ouluvs2 database
using different views of the mouth, frontal to profile. the proposed audiovisual system significantly outperforms the audioonly model for all views when the acoustic noise is high.
index terms: audiovisual fusion, end-to-end deep learning,
audiovisual speech recognition