Examining Bias in Synthetic Speech Detectors Across Gender, Age, Accent, and Speech Impairment

Core Concepts

Existing synthetic speech detectors exhibit significant bias towards certain demographic groups, including gender, age, accent, and speech impairment, leading to unfair misclassification of bona fide speech as synthetic.

Abstract

The authors thoroughly investigate bias in six different synthetic speech detection methods, examining their performance across various demographic groups. The key findings are:

Gender Bias: Most detectors exhibit higher misclassification rates for bona fide speech from male speakers compared to female speakers, especially for older age groups.

Age Bias: Detectors tend to have higher error rates on bona fide speech from younger and older speakers compared to middle-aged speakers.

Accent Bias: Detectors perform worse on bona fide speech with non-US English accents compared to US English accents.

Speech Impairment Bias: Detectors have significantly higher false positive rates on bona fide speech from speakers with speech impairments like stuttering, compared to fluent speakers.

The authors release an evaluation dataset, models, and source code to support future research on addressing these biases in synthetic speech detectors.

Stats

Bona fide speech from male speakers in their 60s has 26.12 percentage points higher False Positive Rate (FPR) compared to female speakers of the same age group for the TSSDNet detector.
The PS3DT detector has around 50 percentage points higher FPR on bona fide speech from male speakers compared to female speakers across different age groups.
The Wav2Vec2-AASIST detector has 11.4 percentage points higher FPR on bona fide speech from male speakers in their 60s compared to female speakers of the same age group.
Bona fide speech from speech-impaired speakers has 10-20 percentage points higher FPR compared to fluent speakers across all detectors.

Quotes

"Several recent incidents have reported misuse of such high-quality synthetic speech for spreading misinformation and being able to commit financial fraud."
"Ensuring fairness in synthetic speech detectors is important to prevent any misclassifications of bona fide speech from a particular ethnic and demographic group as synthetic, when these detectors are deployed on social platforms."

Key Insights Distilled From

FairSSD: Understanding Bias in Synthetic Speech Detectors

by Amit Kumar S... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.10989.pdf

Deeper Inquiries

How can we develop synthetic speech detectors that are robust to demographic biases and ensure fair classification across diverse populations

To develop synthetic speech detectors that are robust to demographic biases and ensure fair classification across diverse populations, several strategies can be implemented:

Diverse and Representative Training Data: Ensure that the training data used to develop the detectors is diverse and representative of the population it aims to serve. This includes a wide range of demographics such as gender, age, accent, and speech characteristics.

Bias Detection and Mitigation: Incorporate bias detection mechanisms during the training phase to identify and mitigate any biases present in the data or model. Techniques such as fairness-aware learning can help in reducing bias in the detectors.

Regular Bias Audits: Conduct regular audits to assess the performance of the detectors across different demographic groups. This can help in identifying and addressing any biases that may arise over time.

Intersectional Analysis: Consider the intersectionality of different demographic factors to ensure that biases are not amplified for individuals who belong to multiple marginalized groups.

Transparency and Accountability: Maintain transparency in the development process of the detectors and ensure accountability for any biases that are identified. This can involve documenting the decision-making process and making the detectors' inner workings accessible for scrutiny.

Continuous Improvement: Implement a feedback loop system where feedback from users and stakeholders is used to continuously improve the detectors and address any biases that are identified post-deployment.

By incorporating these strategies, synthetic speech detectors can be developed to be more robust to demographic biases and ensure fair classification across diverse populations.

What are the potential societal and ethical implications of biased synthetic speech detectors being deployed in real-world applications

Biased synthetic speech detectors being deployed in real-world applications can have significant societal and ethical implications:

Reinforcement of Stereotypes: Biased detectors can reinforce existing stereotypes and prejudices by misclassifying certain demographic groups more frequently, leading to further marginalization and discrimination.

Impact on Decision-Making: If biased detectors are used in critical decision-making processes, such as hiring or criminal justice, they can perpetuate systemic inequalities and lead to unjust outcomes for individuals from marginalized groups.

Erosion of Trust: Biased detectors can erode trust in the technology and the organizations deploying them, leading to a lack of confidence in the fairness and reliability of automated systems.

Social Division: Biased detectors can contribute to social division by creating disparities in access to resources and opportunities based on demographic characteristics rather than merit or need.

Legal and Regulatory Issues: Deployment of biased detectors can raise legal and regulatory concerns, especially regarding data privacy, discrimination, and accountability for the consequences of biased decision-making.

Ethical Responsibility: Organizations developing and deploying synthetic speech detectors have an ethical responsibility to ensure that their technology is fair, unbiased, and does not harm individuals or communities.

Addressing these implications requires a concerted effort to detect, mitigate, and prevent biases in synthetic speech detectors to ensure equitable and just outcomes for all individuals.

How can techniques from other domains, such as debiasing in computer vision or fairness in machine learning, be adapted to address bias in synthetic speech detection

Techniques from other domains, such as debiasing in computer vision and fairness in machine learning, can be adapted to address bias in synthetic speech detection:

Fairness-aware Learning: Adopt techniques from fairness in machine learning to incorporate fairness constraints into the training process of synthetic speech detectors. This can help in reducing bias and ensuring equitable outcomes.

Debiasing Algorithms: Implement debiasing algorithms that can identify and mitigate biases in the data used to train the detectors. Techniques such as reweighing, post-processing, and adversarial debiasing can be applied to address bias in synthetic speech detection.

Intersectional Analysis: Apply intersectional analysis techniques to understand how different demographic factors interact and influence the performance of the detectors. This can help in identifying and mitigating biases that may disproportionately affect certain groups.

Explainable AI: Utilize explainable AI techniques to provide transparency into the decision-making process of the detectors. This can help in identifying and addressing biases that may be present in the model's predictions.

Bias Detection Tools: Implement bias detection tools that can continuously monitor the performance of the detectors and flag any instances of bias. This can enable proactive measures to address bias before it leads to harmful consequences.

By leveraging these techniques and adapting them to the context of synthetic speech detection, developers can work towards creating detectors that are more fair, transparent, and unbiased in their classification across diverse populations.