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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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End-to-End Lip-Reading Open Cloud-Based Speech Architecture.

Sanghun Jeon1, Mun Sang Kim1

  • 1Center for Healthcare Robotics, Gwangju Institute of Science and Technology (GIST), School of Integrated Technology, Gwangju 61005, Korea.

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|April 23, 2022
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Summary
This summary is machine-generated.

This study introduces a novel noise-robust open cloud-based speech recognition (OCSR) API using lip-reading. The enhanced system significantly improves automatic speech recognition (ASR) accuracy in noisy environments.

Keywords:
application programming interfaceaudio-visual speech recognitiondeep neural networkslip-readingmulti-modal interaction

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Signal Processing

Background:

  • Deep learning has advanced noise-robust automatic speech recognition (ASR).
  • Open cloud-based speech recognition application programming interfaces (OCSR APIs) performance is improved by AI and cloud computing.
  • Developing noise-robust ASR for diverse environments is crucial.

Purpose of the Study:

  • Propose noise-robust OCSR APIs using an end-to-end lip-reading architecture for practical, varied environments.
  • Evaluate and integrate leading OCSR APIs (Google, Microsoft, Amazon, Naver) for optimal performance.
  • Enhance keyword semantic information and integrate audio-visual data for improved speech recognition.

Main Methods:

  • Evaluated OCSR APIs using the Google Voice Command Dataset v2.
  • Integrated Microsoft API with Google's word2vec model for enhanced keyword semantics.
  • Developed an audio-visual speech recognition system using a lip-reading architecture with 3D CNN variants.
  • Concatenated API-extracted vectors and visual features for classification.

Main Results:

  • The proposed lip-reading architecture enhanced OCSR API average accuracy by 14.42%.
  • Performance improvements were measured using standard ASR evaluation metrics and signal-to-noise ratio.
  • The model demonstrated superior performance across various noise conditions.

Conclusions:

  • The developed noise-robust OCSR API architecture significantly improves ASR accuracy and dependability in noisy environments.
  • The integration of lip-reading with OCSR APIs offers a practical solution for real-world applications.
  • This approach enhances the reliability of cloud-based speech recognition systems.