LiRA: Learning Visual Speech Representations from Audio through Self-supervision

• URL: http://arxiv.org/abs/2106.09171v1
• Date: Wed, 16 Jun 2021 23:20:06 GMT
• Title: LiRA: Learning Visual Speech Representations from Audio through Self-supervision
• Authors: Pingchuan Ma, Rodrigo Mira, Stavros Petridis, Bj\"orn W. Schuller and Maja Pantic
• Abstract summary: We propose Learning visual speech Representations from Audio via self-supervision (LiRA) Specifically, we train a ResNet+Conformer model to predict acoustic features from unlabelled visual speech. We show that our approach significantly outperforms other self-supervised methods on the Lip Reading in the Wild dataset.
• Score: 53.18768477520411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The large amount of audiovisual content being shared online today has drawn substantial attention to the prospect of audiovisual self-supervised learning. Recent works have focused on each of these modalities separately, while others have attempted to model both simultaneously in a cross-modal fashion. However, comparatively little attention has been given to leveraging one modality as a training objective to learn from the other. In this work, we propose Learning visual speech Representations from Audio via self-supervision (LiRA). Specifically, we train a ResNet+Conformer model to predict acoustic features from unlabelled visual speech. We find that this pre-trained model can be leveraged towards word-level and sentence-level lip-reading through feature extraction and fine-tuning experiments. We show that our approach significantly outperforms other self-supervised methods on the Lip Reading in the Wild (LRW) dataset and achieves state-of-the-art performance on Lip Reading Sentences 2 (LRS2) using only a fraction of the total labelled data.

Related papers

• Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping [4.271091833712731]
  We propose a simple approach, named Lip2Vec that is based on learning a prior model. The proposed model compares favorably with fully-supervised learning methods on the LRS3 dataset achieving 26 WER. We believe that reprogramming the VSR as an ASR task narrows the performance gap between the two and paves the way for more flexible formulations of lip reading.
  arXiv  Detail & Related papers  (2023-08-11T12:59:02Z)
• AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR [79.21857972093332]
  We present AVFormer, a method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively.
  arXiv  Detail & Related papers  (2023-03-29T07:24:28Z)
• Jointly Learning Visual and Auditory Speech Representations from Raw Data [108.68531445641769]
  RAVEn is a self-supervised multi-modal approach to jointly learn visual and auditory speech representations. Our design is asymmetric w.r.t. driven by the inherent differences between video and audio. RAVEn surpasses all self-supervised methods on visual speech recognition.
  arXiv  Detail & Related papers  (2022-12-12T21:04:06Z)
• Sub-word Level Lip Reading With Visual Attention [88.89348882036512]
  We focus on the unique challenges encountered in lip reading and propose tailored solutions. We obtain state-of-the-art results on the challenging LRS2 and LRS3 benchmarks when training on public datasets. Our best model achieves 22.6% word error rate on the LRS2 dataset, a performance unprecedented for lip reading models.
  arXiv  Detail & Related papers  (2021-10-14T17:59:57Z)
• Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning [17.6311804187027]
  An underlying correlation between audio and visual events can be utilized as free supervised information to train a neural network. We propose a novel self-supervised framework with co-attention mechanism to learn generic cross-modal representations from unlabelled videos. Experiments show that our model achieves state-of-the-art performance on the pretext task while having fewer parameters compared with existing methods.
  arXiv  Detail & Related papers  (2020-08-13T10:08:12Z)
• Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision [63.564385139097624]
  We propose a method to learn self-supervised speech representations from the raw audio waveform. We train a raw audio encoder by combining audio-only self-supervision (by predicting informative audio attributes) with visual self-supervision (by generating talking faces from audio) Our results demonstrate the potential of multimodal self-supervision in audiovisual speech for learning good audio representations.
  arXiv  Detail & Related papers  (2020-07-08T14:07:06Z)
• Visually Guided Self Supervised Learning of Speech Representations [62.23736312957182]
  We propose a framework for learning audio representations guided by the visual modality in the context of audiovisual speech. We employ a generative audio-to-video training scheme in which we animate a still image corresponding to a given audio clip and optimize the generated video to be as close as possible to the real video of the speech segment. We achieve state of the art results for emotion recognition and competitive results for speech recognition.
  arXiv  Detail & Related papers  (2020-01-13T14:53:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.