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End-to-end audiovisual speech recognition

Petridis, Stavros; Stafylakis, Themos; Ma, Pingchuan; Cai, Feipeng; Tzimiropoulos, Georgios; Pantic, Maja


Stavros Petridis

Themos Stafylakis

Pingchuan Ma

Feipeng Cai

Georgios Tzimiropoulos

Maja Pantic


Several end-to-end deep learning approaches have been recently presented which extract either audio or visual features from the input images or audio signals and perform speech recognition. However, research on end-to-end audiovisual models is very limited. In this work, we present an end-to-end audiovisual model based on residual networks and Bidirectional Gated Recurrent Units (BGRUs). To the best of our knowledge, this is the first audiovisual fusion model which simultaneously learns to extract features directly from the image pixels and audio waveforms and performs within-context word recognition on a large publicly available dataset (LRW). The model consists of two streams, one for each modality, which extract features directly from mouth regions and raw waveforms. The temporal dynamics in each stream/modality are modeled by a 2-layer BGRU and the fusion of multiple streams/modalities takes place via another 2-layer BGRU. A slight improvement in the classification rate over an end-to-end audio-only and MFCC-based model is reported in clean audio conditions and low levels of noise. In presence of high levels of noise, the end-to-end audiovisual model significantly outperforms both audio-only models.


Petridis, S., Stafylakis, T., Ma, P., Cai, F., Tzimiropoulos, G., & Pantic, M. (2018). End-to-end audiovisual speech recognition.

Conference Name IEEE International Conference on Acoustics, Speech, and Signal Processing
End Date Apr 20, 2018
Acceptance Date Jan 20, 2018
Publication Date Apr 15, 2018
Deposit Date Apr 13, 2018
Publicly Available Date Apr 15, 2018
Peer Reviewed Peer Reviewed
Public URL


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