Title

LipLearner: Customizable Silent Speech Interactions on Mobile Devices

Bibliographic Information

• Research Area: Human-Computer Interaction and Artificial Intelligence, with a focus on silent speech interfaces and their applications on mobile devices
• Keywords: Silent speech interface, lip-reading technology, few-shot learning, customization, mobile devices, feature representation, online learning

Research Background and Problem Statement

• Core Issues or Challenges:

  • Traditional voice user interfaces (VUIs) face privacy and social acceptability issues, particularly in public settings such as subways or meeting rooms.
  • Noisy environments or speech impairments limit the effectiveness of voice data collection and recognition.
  • Conventional silent speech interfaces typically require large amounts of data, long training times, and high technical costs. Additionally, their vocabularies are often limited and rigid, failing to fully meet user needs.

• Significance of the Research:

  • Silent speech interfaces can reduce privacy risks, enhance user comfort, and enable seamless interaction with devices.
  • Developing low-barrier solutions for existing mobile devices (e.g., smartphones) can expand the practical applications of silent speech technology.

• Motivation and Related Work:

  • While existing studies have explored various signal acquisition methods (e.g., electromyography, capacitive sensors, and ultrasound imaging), lip-reading technology offers advantages such as low device requirements, rich information, and suitability for a wide range of scenarios.
  • Recent advancements in deep learning and contrastive learning have shown promise in visual speech processing, but few-shot learning and customizable commands remain emerging needs.

Proposed Solution

• Methods and Innovations:

  • A large-scale lip representation model based on contrastive learning is proposed to improve few-shot learning performance and generalization capabilities.
  • Data representation combines public datasets (LRW) with real-world mobile scenarios, using data augmentation to bridge the distribution gap between studio data and natural interaction environments.
  • A mobile application, “LipLearner,” is developed to provide streaming learning, user-defined commands, and visual keyword detection.

• Implementation Steps and Techniques:

  1. Model Pretraining: Pretrain on a large public lip-reading dataset using contrastive learning with a predefined vocabulary.
  2. Few-Shot Fine-Tuning: Use a linear classifier to quickly adapt to new commands with minimal user data, reducing the required sample size.
  3. Visual Keyword Detection: Implement a cosine similarity-based detection algorithm to trigger keywords and segment commands based on user-defined lip movements.
  4. Online Incremental Learning: Dynamically optimize performance by adding samples during user interactions, with one-click model updates to ensure real-time adaptability.

Research Outcomes

• Key Findings:

  • Experiments demonstrate that the few-shot learning framework achieves 81.7%-98.8% accuracy with only one to five data samples, significantly outperforming traditional user-dependent or predefined silent speech systems.
  • The lip-reading model exhibits high environmental robustness, adapting to variations in lighting, user posture, and smartphone handling.
  • A mobile device-based prototype system is provided, supporting real-time silent speech command customization and efficient learning while meeting privacy protection requirements.

• Advantages Over Existing Solutions:

  • The convenience of adding new commands is significantly improved. Traditional methods require extensive user data and retraining the model, whereas this study employs rapid few-shot fine-tuning.
  • The system demonstrates superior robustness in real-world environments (e.g., low light, shaky video) compared to existing solutions.
  • User experience evaluations indicate the system offers high usability, learnability, and enjoyment.

• Experiments and Evaluation Results:

  • Keyword Detection: Average error rate across commands is 6.75%, with dynamic threshold adjustment and user feedback enabling rapid optimization.
  • User Study: Supports linguistic diversity (e.g., Chinese, English, Japanese), showing significant flexibility and creativity in user-defined commands.
  • Usability Survey: System Usability Scale (SUS) score of 84.8 indicates high acceptability.

• Limitations and Future Directions:

  • Physical and Cognitive Load: High-intensity learning modes may impact user experience. Future work will optimize interaction design to reduce user burden.
  • Low Semantic Abstraction: Complex commands require detailed registration. Incorporating zero-shot learning and language models will expand interaction levels and semantic understanding.
  • Hardware Adaptation: Currently focused on smartphones, further research will explore the potential for adapting the technology to wearable devices (e.g., VR/AR headsets).

The above findings provide guidance for related research and practical applications, contributing to advancements in the field of human-computer interaction.