Ensuring the accessibility of web forms is crucial in today’s interconnected society, particularly for individuals who are illiterate, physically impaired, or face language barriers. This paper aims to enhance web form accessibility by presenting a novel approach that transforms traditional web forms into interactive audio formats using a microservices architecture that integrates advanced technologies. MarianMT enables seamless translation of both user inputs and form questions, while Mozilla TTS provides text-to-speech synthesis, allowing users with reading difficulties to hear form questions aloud. Additionally, Whisper’s speech recognition technology converts spoken responses into text. At the core of the system, a natural language processing (NLP) module based on JointBERT, fine-tuned with domain-specific data, accurately detects user intent and extracts key information (slots) for form completion. Testing demonstrated promising results, achieving 74% accuracy in intent detection and 70% accuracy in slot extraction. These results highlight the system’s potential to enhance web form accessibility, creating a more seamless and inclusive user experience.

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From Textual Web Forms to Audio Web Forms: Towards a Microservices Architecture that Leverages Natural Language Processing Techniques

  • Jean Louis K. E. Fendji,
  • Moussa Hassana,
  • Marcellin Atemkeng

摘要

Ensuring the accessibility of web forms is crucial in today’s interconnected society, particularly for individuals who are illiterate, physically impaired, or face language barriers. This paper aims to enhance web form accessibility by presenting a novel approach that transforms traditional web forms into interactive audio formats using a microservices architecture that integrates advanced technologies. MarianMT enables seamless translation of both user inputs and form questions, while Mozilla TTS provides text-to-speech synthesis, allowing users with reading difficulties to hear form questions aloud. Additionally, Whisper’s speech recognition technology converts spoken responses into text. At the core of the system, a natural language processing (NLP) module based on JointBERT, fine-tuned with domain-specific data, accurately detects user intent and extracts key information (slots) for form completion. Testing demonstrated promising results, achieving 74% accuracy in intent detection and 70% accuracy in slot extraction. These results highlight the system’s potential to enhance web form accessibility, creating a more seamless and inclusive user experience.