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Original Paper: https://arxiv.org/abs/2302.08102
Abstract:
Visual Speech Recognition (VSR) aims to infer speech into text depending on lip movements alone. As it focuses on visual information to model the speech, its performance is inherently sensitive to personal lip appearances and movements, and this makes the VSR models show degraded performance when they are applied to unseen speakers. In this paper, to remedy the performance degradation of the VSR model on unseen speakers, we propose prompt tuning methods of Deep Neural Networks (DNNs) for speaker-adaptive VSR. Specifically, motivated by recent advances in Natural Language Processing (NLP), we finetune prompts on adaptation data of target speakers instead of modifying the pre-trained model parameters. Different from the previous prompt tuning methods mainly limited to Transformer variant architecture, we explore different types of prompts, the addition, the padding, and the concatenation form prompts that can be applied to the VSR model which is composed of CNN and Transformer in general. With the proposed prompt tuning, we show that the performance of the pre-trained VSR model on unseen speakers can be largely improved by using a small amount of adaptation data (e.g., less than 5 minutes), even if the pre-trained model is already developed with large speaker variations. Moreover, by analyzing the performance and parameters of different types of prompts, we investigate when the prompt tuning is preferred over the finetuning methods. The effectiveness of the proposed method is evaluated on both word- and sentence-level VSR databases, LRW-ID and GRID.
Summary Notes
Improving Speaker Adaptability in Visual Speech Recognition Using Prompt Tuning
Introduction
Visual Speech Recognition (VSR) turns lip movements into text, a crucial technology when audio isn't available. However, adapting VSR systems to new, unseen speakers is challenging due to their reliance on visual cues.
This post discusses prompt tuning as a novel, scalable, and data-efficient solution to improve speaker adaptability in VSR.
Background
Deep learning models like CNNs and Transformers have propelled VSR forward by enhancing visual feature encoding and temporal dynamics.
Yet, adapting to new speakers remains a hurdle, with traditional methods requiring extensive data or model alterations.
Drawing inspiration from Automatic Speech Recognition (ASR), we explore how prompt tuning can be applied to VSR for better speaker adaptability.
Implementing Prompt Tuning for VSR
Prompt tuning involves minor, learnable adjustments to pre-trained models, making them more adaptable with less data. We focus on three prompt types:
- Addition Prompts: Attached to input frames to help with feature extraction, making the model more adaptable to new speakers.
- Padding Prompts: Modify convolutional layer padding to improve feature representation for new speakers.
- Concatenation Prompts: Used in Transformers to enhance temporal feature modeling, essential for speech-to-text conversion.
Benefits of prompt tuning include minimal parameter changes, less retraining, and better adaptation to new speakers with fewer data.
Experimental Methodology
We tested our approach on the LRW and GRID datasets for word and sentence VSR tasks, using pre-trained models adapted with prompt tuning and a small amount of speaker-specific data. This method allowed us to evaluate the effectiveness of different prompts.
Findings
Prompt tuning significantly improved performance for unseen speakers with limited data:
- Padding Prompts were most effective in deeper CNNs, enhancing feature representation.
- Addition Prompts worked well in shallower networks, optimizing feature extraction.
- Concatenation Prompts boosted Transformer models by improving temporal modeling.
Prompt tuning proved more scalable and efficient than traditional adaptation methods, showing great promise for VSR applications.
Conclusion
Prompt tuning marks a major advancement in adapting VSR systems to unseen speakers with minimal data and computational resources.
This approach's scalability and efficiency offer new opportunities for deploying advanced VSR in various settings, addressing the challenge of speaker variability.
Future research into integrating prompt tuning into more complex VSR systems could further advance speech recognition technology.
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