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Speech Intention Classification with Multimodal Deep Learning.

Yue Gu1, Xinyu Li1, Shuhong Chen1

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|December 4, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning model for speech classification using text and audio data. The novel approach achieved 83.10% accuracy in identifying medical intentions, outperforming previous methods.

Keywords:
Convolutional neural networkMultimodal intention classificationTextual-acoustic feature representationTrauma resuscitation

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Area of Science:

  • Artificial Intelligence
  • Speech Processing
  • Machine Learning

Background:

  • Accurate speech classification is crucial in various applications, including healthcare.
  • Existing methods often rely on manually engineered features, limiting their adaptability and performance.
  • Multimodal data integration offers potential for enhanced speech understanding.

Purpose of the Study:

  • To develop and evaluate a novel multimodal deep learning model for sentence-level speech classification.
  • To automatically extract features from textual and acoustic data for improved intention detection.
  • To assess the model's performance in a real-world medical setting.

Main Methods:

  • A multimodal deep learning architecture was designed, employing independent convolutional neural networks for textual and acoustic feature extraction.
  • Features were combined into a joint representation and processed by a decision softmax layer.
  • The model was trained and validated using speech recordings and transcribed logs from a medical environment.

Main Results:

  • The proposed model achieved an average accuracy of 83.10% in detecting six distinct intentions.
  • Automatic feature extraction demonstrated superior performance compared to models utilizing manufactured features.
  • The system proved effective in a practical medical setting.

Conclusions:

  • The novel multimodal deep learning model offers a robust and accurate solution for speech intention classification.
  • Automatic feature extraction from textual-acoustic data is a promising direction for advancing speech processing in medical applications.
  • This approach surpasses traditional methods, paving the way for more sophisticated human-computer interaction in healthcare.