JEP-KD: A Joint-Embedding Predictive Architecture for Enhancing Visual Speech Recognition through Knowledge Distillation

Conceitos Básicos

The paper introduces JEP-KD, a joint-embedding predictive architecture that leverages a generative network within the embedding layer to enhance the video encoder's capacity for semantic feature extraction and better align it with audio features from a pre-trained ASR model. This approach aims to progressively reduce the performance gap between visual speech recognition (VSR) and automatic speech recognition (ASR).

Resumo

The paper proposes a novel knowledge distillation framework for visual speech recognition (VSR) called JEP-KD, which stands for Joint-Embedding Predictive Architecture-based Knowledge Distillation. The key innovation is the inclusion of a generative network within the embedding layer, which serves to enhance the video encoder's ability to extract semantic features and better align them with the audio features from a pre-trained ASR model.

The authors argue that the prevalent knowledge distillation methods for VSR, which rely on rigid alignment of video and audio features, are suboptimal due to the inherent semantic limitations of the video modality. They hypothesize that the semantic gaps between video and audio are systematic and predictable, and can be addressed by the proposed predictive framework.

The JEP-KD architecture consists of four main components: the video encoder, the generator, the discriminator, and the decoder. The training process is divided into three stages:

1. Warm-up phase: Train the encoder, generator, and decoder with CTC and CE losses.
2. Enhancement stage: Lock the encoder and decoder, train the generator and discriminator using adversarial loss and a distance loss between video and audio semantic features.
3. Refinement phase: Lock the encoder, generator, and discriminator, and fine-tune the decoder.

Experiments on the CMLR dataset show that the JEP-KD framework significantly improves the performance of VSR models, reducing the character error rate (CER) from 19.92% to 14.26%. Further pre-training on additional datasets leads to a CER of 11.97%, demonstrating the versatility of the approach. However, the authors note that there is still a substantial gap compared to ASR models, suggesting room for further research to enhance the predictive capabilities of the model.

Estatísticas

The CMLR dataset contains 102,072 spoken sentences, including 71,448 in the training set, 20,418 in the test set, and 10,206 in the validation set.
The ASR model (WeNet) achieves around 2% CER on the CMLR dataset without fine-tuning.

Citações

"Utilizing trained ASR models to conduct knowledge distillation is a widely acknowledged and efficacious strategy to enhance performance."
"We hold that the semantic gaps manifesting in video modalities relative to audio ones adhere to a systematic pattern — that is, these consistent omissions are correlated with sentence content and are, therefore, predictable."
"Considering the considerable performance gap that still exists between VSR and ASR, enhancing the capabilities of VSR as much as possible remains the most important research objective at the current stage."

Principais Insights Extraídos De

JEP-KD

by Chang Sun,Ho... às arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.18843.pdf

Perguntas Mais Profundas

How can the predictive capabilities of the JEP-KD framework be further enhanced to narrow the gap between VSR and ASR performance?

To further enhance the predictive capabilities of the JEP-KD framework and narrow the performance gap between Visual Speech Recognition (VSR) and Automatic Speech Recognition (ASR), several strategies can be implemented. Firstly, refining the generator model within the JEP-KD architecture to better predict the semantic variances across modalities is crucial. This can involve optimizing the generator's ability to translate video semantic features into audio semantic features accurately. Additionally, incorporating more advanced techniques such as reinforcement learning or attention mechanisms can help improve the predictive capabilities of the framework. By enhancing the generator's capacity to simulate the semantic loss of the video modality compared to the speech modality, the JEP-KD framework can better align the semantic representations from both modalities, thus improving overall performance.

What other multimodal tasks could benefit from the JEP-KD architecture, and how would the training process need to be adapted?

The JEP-KD architecture's unique approach to knowledge distillation and predictive modeling can benefit various other multimodal tasks beyond Visual Speech Recognition (VSR). Tasks such as audio-visual emotion recognition, gesture recognition, and audio-visual event detection could leverage the JEP-KD framework. To adapt the training process for these tasks, the models would need to be tailored to the specific input modalities involved. For instance, in audio-visual emotion recognition, the training data would consist of audio and visual emotional cues, and the JEP-KD framework would need to predict the emotional content across both modalities. The training regimen would involve adjusting the loss functions and model architectures to suit the characteristics of each multimodal task, ensuring effective knowledge distillation and predictive learning.

Could the JEP-KD framework be combined with an AVSR model as the teacher network to provide even stronger guidance for the VSR model?

Yes, combining the JEP-KD framework with an Audio-Visual Speech Recognition (AVSR) model as the teacher network can indeed provide stronger guidance for the VSR model. By integrating the AVSR model's expertise in processing both audio and visual information, the teacher network can offer more comprehensive guidance during the knowledge distillation process. The AVSR model can help the VSR model better align its semantic representations with those of the ASR model, leading to improved performance in lip-reading tasks. This combined approach can enhance the VSR model's ability to extract meaningful features from both audio and visual inputs, ultimately bridging the performance gap between VSR and ASR.