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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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BAT: Block and token self-attention for speech emotion recognition.

Jianjun Lei1, Xiangwei Zhu1, Ying Wang1

  • 1School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 15, 2022
PubMed
Summary

This study introduces a novel Block and Token Self-Attention (BAT) model for speech emotion recognition (SER). BAT enhances noise resilience and accuracy by processing spectrograms in blocks and tokens, outperforming existing methods.

Keywords:
Self-attentionSpeech emotion recognitionTransformer

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Area of Science:

  • Artificial Intelligence
  • Speech Emotion Recognition
  • Deep Learning Architectures

Background:

  • Transformer architectures are successful in AI but struggle with noise and local information in speech emotion recognition (SER).
  • Current token-by-token attention in SER lacks capacity for local emotion cues and is susceptible to noise.
  • Existing methods require further improvement for robust and accurate SER.

Purpose of the Study:

  • To propose a novel SER architecture, Block and Token Self-Attention (BAT), to improve noise resilience and capture local emotion information.
  • To introduce a cross-block attention mechanism and a frequency compression and channel enhancement (FCCE) module for enhanced information interaction and attention smoothing.
  • To evaluate the performance of the BAT model on benchmark datasets and assess its cross-database and cross-domain capabilities.

Main Methods:

  • The proposed BAT architecture splits spectrograms into blocks and computes self-attention by combining blocks with tokens.
  • A cross-block attention mechanism facilitates information exchange between different blocks.
  • A frequency compression and channel enhancement (FCCE) module is integrated to mitigate attention biases.

Main Results:

  • BAT achieved 73.2% weighted accuracy (WA) and 75.2% unweighted accuracy (UA) on the IEMOCAP dataset, surpassing prior state-of-the-art results.
  • The model demonstrated strong performance on cross-database tasks, achieving 89% WA and 87.4% UA on Emo-DB.
  • BAT attained 88.32% top-1 accuracy on the CIFAR-10 image dataset with minimal parameters (15.01 Mb) without data augmentation or pretraining.

Conclusions:

  • The BAT architecture effectively alleviates noise effects and captures authentic sentiment expressions in SER.
  • The proposed method shows significant potential for cross-database and cross-domain SER applications.
  • BAT offers a computationally efficient and high-performing solution for emotion recognition across different modalities.