Speech Emotion Recognition With Adversarial Bias Removal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech emotion recognition technologies suffer from evaluator bias due to subjective interpretations by annotators, leading to inconsistent and biased model performance, and lack transparency in fairness, hindering practical applications.
Innovation Solution
A fairness-constrained adversarial network (FCAN) is employed, utilizing a domain classifier and speech emotion recognition model trained with a Wasserstein distance loss to minimize bias, generating fair embeddings and predictions, and a multi-perspective model using metric learning losses for consistent predictions across different perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech emotion recognition models are trained on annotated data, then the model can perform emotion recognition, but evaluator bias is introduced leading to unfair and inconsistent results
Solution Approach 1:
The patent extracts and removes bias from the training process by introducing a domain classifier that specifically targets and separates biased features from the speech data. The bias removal module extracts unbiased representations by filtering out evaluator-specific patterns, allowing the model to learn emotion recognition without inheriting annotator biases.
Solution Approach 2:
The patent introduces an intermediary bias removal module between the speech input and the emotion recognition model. This intermediary component processes the speech data through multiple perspectives and synthesizes an unbiased representation, acting as a mediator that prevents bias transmission from training data to predictions.
2Reliability
If domain-specific bias correction methods are applied, then bias in specific domains can be reduced, but the methods cannot be universally applied and require more training data
Solution Approach 1:
The patent creates a universal bias removal framework that can be applied across different domains and datasets. The multi-perspective training approach and domain classifier are designed to work with any speech emotion recognition task, making the bias correction mechanism domain-agnostic and universally applicable without requiring domain-specific adjustments or additional training data.
Solution Approach 2:
The patent changes the training parameters by introducing perspective weights and domain classification targets that can be adjusted across different domains. The framework allows dynamic parameter adjustment to accommodate various speech datasets and emotion categories while maintaining the core bias removal functionality.
3Device complexity
If machine learning systems operate as black boxes, then model complexity is reduced, but transparency and user understanding of fairness are lost
Solution Approach 1:
The patent implements feedback mechanisms that provide users with information about how predictions are made. The system can indicate which perspective was most influential in a given prediction and provide explanations for bias removal decisions, creating a feedback loop that enhances transparency without significantly increasing model complexity.
Solution Approach 2:
The patent segments the emotion recognition process into distinct components: bias detection, multi-perspective analysis, and unbiased prediction. This segmentation allows each component to be independently understood and explained, making the overall system more transparent while maintaining manageable complexity through modular design.
Data Source
AI summary
A computer-implemented method for speech emotion recognition is provided. The computer-implemented method includes an emotion prediction corresponding to speech data that is generated based on the speech data and a speech emotion recognition model without bias. The speech emotion recognition model without bias is trained by training a fairness-constrained adversarial network based on a labeled training set with known bias and a loss function. The fairness-constrained adversarial network includes a domain classifier for bias classification and the speech emotion recognition model. The loss function used for training the fairness-constrained adversarial network is positively related to a Wasserstein distance (WD) loss.