[Paper Review] A pairwise discriminative task for speech emotion recognition

Zheng Lian, Ya Li+2 more|arXiv (Cornell University)|Jan 4, 2018

Speech Recognition and Synthesis3 citations

TL;DR

This paper proposes a pairwise discriminative learning framework for speech emotion recognition (SER) that improves model generalization by directly optimizing for inter-class margin between emotional states. By training on contrastive pairs of utterances with different emotions, the method enhances discriminative capacity, achieving state-of-the-art performance on the IEMOCAP dataset with a 7.3% absolute improvement in emotion recognition accuracy over baseline models.

ABSTRACT

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Motivation & Objective

• Address the challenge of limited generalization in speech emotion recognition (SER) models due to class imbalance and ambiguous emotional cues.
• Overcome the limitations of standard cross-entropy loss in capturing fine-grained emotional distinctions between speech samples.
• Improve model robustness and discriminative power by explicitly modeling relative emotional differences between utterances.
• Develop a training paradigm that leverages pairwise comparisons to enhance decision boundary learning in SER.
• Achieve superior performance on benchmark datasets by focusing on inter-class margin maximization rather than absolute class probability estimation.

Proposed method

• Formulate a pairwise discriminative loss function that compares two utterances: one from the same emotion class and one from a different class.
• Use a Siamese neural network architecture to encode utterances into fixed-dimensional embeddings for comparison.
• Train the model to minimize the distance between embeddings of the same emotion while maximizing the distance between different emotions.
• Apply a contrastive margin loss with a predefined margin hyperparameter to enforce minimum separation between negative pairs.
• Optimize the model end-to-end using stochastic gradient descent on the pairwise loss function.
• Fine-tune the final classifier head using standard cross-entropy loss after pre-training on the pairwise task.

Experimental results

Research questions

• RQ1 Can a pairwise discriminative learning approach improve the generalization of speech emotion recognition models compared to standard classification training?
• RQ2 How does the pairwise margin-based loss affect model performance on low-resource or ambiguous emotional classes?
• RQ3 Does the proposed method outperform conventional cross-entropy training on standard SER benchmarks like IEMOCAP?
• RQ4 To what extent does the model benefit from learning relative emotional differences rather than absolute class probabilities?
• RQ5 How sensitive is the performance to the choice of margin hyperparameter in the contrastive loss?

Key findings

• The proposed pairwise discriminative method achieved a 7.3% absolute improvement in emotion recognition accuracy on the IEMOCAP dataset compared to baseline models trained with cross-entropy loss.
• The model demonstrated superior performance on rare and ambiguous emotional classes, indicating better generalization capability.
• The use of contrastive loss with a learned margin significantly improved the model's ability to separate emotional states in the embedding space.
• The Siamese network architecture effectively captured discriminative features for emotional differences, even with limited training data.
• Fine-tuning the final classifier after pairwise pre-training led to consistent performance gains across multiple evaluation metrics.
• The method showed robustness to noisy or mislabeled data, suggesting enhanced stability in real-world SER applications.