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Articulation constrained learning with application to speech emotion recognition.

Mohit Shah1, Ming Tu2, Visar Berisha1,2

  • 1School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA.

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|December 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel speech emotion recognition method using both articulatory and acoustic data. The approach enhances emotion classification accuracy, particularly for valence and distinguishing happiness.

Keywords:
ArticulationConstrained optimizationCross-corpusEmotion recognition

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Area of Science:

  • Computer Science
  • Speech Processing
  • Machine Learning

Background:

  • Combining articulatory and acoustic features improves speech emotion recognition.
  • Large-scale articulatory data collection is often infeasible, limiting current methods.

Purpose of the Study:

  • To propose a discriminative learning method for emotion recognition using both articulatory and acoustic information.
  • To develop a model that reconstructs articulatory data while optimizing for emotion recognition.

Main Methods:

  • Extended a traditional L1-regularized logistic regression cost function with reconstruction constraints.
  • Jointly optimized sparse representations for both articulatory reconstruction and emotion recognition.
  • Utilized only articulatory features during training and speech features during inference.

Main Results:

  • Significantly improved performance for valence-based classification by incorporating articulatory information.
  • Demonstrated enhanced effectiveness in distinguishing happiness from other emotions in categorical emotion recognition.
  • Achieved strong results in both within-corpus and cross-corpus experiments.

Conclusions:

  • The proposed method effectively integrates articulatory and acoustic information for robust speech emotion recognition.
  • The model's ability to reconstruct articulatory data leads to improved and interpretable emotion classification.
  • This approach overcomes the limitations of large-scale articulatory data collection for practical applications.