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Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading

Samar Daou, Achraf Ben-Hamadou, Ahmed Rekik, Abdelaziz Kallel

TL;DR

This article introduces a cross-attention mechanism that combines visual and geometric information to obtain an optimal representation of lip movements for lipreading tasks, and proposes extracting optimal visual features using 3D convolution blocks followed by a ResNet-18.

Abstract

Lipreading involves using visual data to recognize spoken words by analyzing the movements of the lips and surrounding area. It is a hot research topic with many potential applications, such as human-machine interaction and enhancing audio speech recognition. Recent deep-learning based works aim to integrate visual features extracted from the mouth region with landmark points on the lip contours. However, employing a simple combination method such as concatenation may not be the most effective approach to get the optimal feature vector. To address this challenge, firstly, we propose a cross-attention fusion-based approach for large lexicon Arabic vocabulary to predict spoken words in videos. Our method leverages the power of cross-attention networks to efficiently integrate visual and geometric features computed on the mouth region. Secondly, we introduce the first large-scale Lipreading in the Wild for Arabic (LRW-AR) dataset containing 20,000 videos for 100-word classes, uttered by 36 speakers. The experimental results obtained on LRW-AR and ArabicVisual databases showed the effectiveness and robustness of the proposed approach in recognizing Arabic words. Our work provides insights into the feasibility and effectiveness of applying lipreading techniques to the Arabic language, opening doors for further research in this field. Link to the project page: https://crns-smartvision.github.io/lrwar

Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading

TL;DR

This article introduces a cross-attention mechanism that combines visual and geometric information to obtain an optimal representation of lip movements for lipreading tasks, and proposes extracting optimal visual features using 3D convolution blocks followed by a ResNet-18.

Abstract

Lipreading involves using visual data to recognize spoken words by analyzing the movements of the lips and surrounding area. It is a hot research topic with many potential applications, such as human-machine interaction and enhancing audio speech recognition. Recent deep-learning based works aim to integrate visual features extracted from the mouth region with landmark points on the lip contours. However, employing a simple combination method such as concatenation may not be the most effective approach to get the optimal feature vector. To address this challenge, firstly, we propose a cross-attention fusion-based approach for large lexicon Arabic vocabulary to predict spoken words in videos. Our method leverages the power of cross-attention networks to efficiently integrate visual and geometric features computed on the mouth region. Secondly, we introduce the first large-scale Lipreading in the Wild for Arabic (LRW-AR) dataset containing 20,000 videos for 100-word classes, uttered by 36 speakers. The experimental results obtained on LRW-AR and ArabicVisual databases showed the effectiveness and robustness of the proposed approach in recognizing Arabic words. Our work provides insights into the feasibility and effectiveness of applying lipreading techniques to the Arabic language, opening doors for further research in this field. Link to the project page: https://crns-smartvision.github.io/lrwar
Paper Structure (36 sections, 1 equation, 10 figures, 7 tables)

This paper contains 36 sections, 1 equation, 10 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Lipreading datasets
  4. Large-scale lipreading datasets
  5. Lipreading datasets for Arabic language
  6. Lipreading methods
  7. Lipreading methods for Arabic language
  8. Lipreading methods for other languages
  9. Attention mechanisms for lipreading
  10. Our method positioning
  11. Proposed approach
  12. Input data preprocessing
  13. Visual-feature network
  14. Geometric-feature network
  15. FusionNet network
  16. ...and 21 more sections

Figures (10)

  • Figure 1: Outline of the proposed approach. Video preprocessing: crop the mouth region from the input video sequence and obtain the corresponding facial landmarks. Visual-feature network: extract relevant information from the preprocessed data. Geometric-feature network: encodes lip contour variation delivered by facial landmarks. FusionNet network: fuses the encoded features. Sequence back-end network: based on a multi-scale temporal convolutional network (MS-TCN) to encode temporal variation and classify the input video sequence.
  • Figure 2: Architecture of the FusionNet: multi-modality cross-attention fusion network.
  • Figure 3: Sequence decoder network with four layers of multi-scale expanded TCN with 1, 2, 4, and 8 dilation values, respectively. Each layer is composed of three TCN units with 3, 5, and 7 kernel values.
  • Figure 4: Pipeline to generate the LRW-AR dataset.
  • Figure 5: Frame extraction and face alignment process.
  • ...and 5 more figures