Gender Disparities in Multilingual Speech Recognition Models

Core Concepts

Multilingual ASR models exhibit gender performance gaps, favoring different genders across languages, highlighting persistent disparities despite advancements.

Abstract

The study evaluates gender disparities in multilingual ASR models, revealing preferences for male or female speakers depending on the language. The analysis uses probes to understand how models encode gender information and suggests potential proxies for measuring and addressing gender gaps. The research highlights the need for more representative datasets and group-aware splits to address fairness concerns. It also discusses ethical considerations regarding the use of gender as a variable and limitations related to data quality and methodology.

Stats

Using two popular models on three datasets in 19 languages across seven language families. Average error rates across language families and datasets. Logistic probe F1 scores for gender extractability. Correlation between probing scores and error rate gaps.

Quotes

"Our results show that group disparities remain unsolved despite great progress on multi-tasking and multilinguality." "Models consistently exhibit gender bias across 19 languages from seven language families." "We locate a potential source of these gaps using gender probes from interpretability approaches."

Key Insights Distilled From

Multilingual Speech Models for Automatic Speech Recognition Exhibit Gender Performance Gaps

by Giuseppe Att... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17954.pdf

Deeper Inquiries

How can dataset releases factor in collecting samples from less represented groups?

In order to address the issue of underrepresentation of certain groups in datasets, dataset releases can implement several strategies. Firstly, they can prioritize inclusivity by actively seeking out and including diverse voices from marginalized or underrepresented communities. This may involve reaching out to specific communities, partnering with organizations that represent these groups, or providing incentives for participation. Secondly, dataset releases can ensure balanced representation by stratifying data collection efforts based on demographic factors such as gender identity, ethnicity, age, and socioeconomic background. By intentionally collecting samples from less represented groups and ensuring a proportional distribution across all categories, datasets can better reflect the diversity present in real-world populations. Moreover, dataset releases should provide clear guidelines and protocols for data collection to ensure consistency and fairness. This includes establishing ethical standards for obtaining consent, protecting privacy rights, and addressing any potential biases that may arise during the data collection process. By incorporating these considerations into dataset releases, researchers can create more inclusive and representative datasets that account for the diversity of human experiences.

What are the implications of using probes as a proxy for measuring and mitigating gender disparities?

Using probes as a proxy for measuring and mitigating gender disparities in models like Automatic Speech Recognition (ASR) has both benefits and limitations. Probes offer a way to analyze how models encode different inputs based on sensitive attributes like gender. By examining how well models differentiate between male-identified speakers versus female-identified speakers through probing techniques like logistic regression or Minimum Description Length (MDL), researchers gain insights into potential biases within the model's internal representations. One implication is that high extractability of gender information from model embeddings could indicate an increased risk of bias towards one gender group over another in ASR performance. This insight allows researchers to identify areas where disparities exist and take steps to mitigate them through targeted interventions such as retraining models on more diverse datasets or adjusting algorithms to reduce bias. However, it's essential to recognize that using probes comes with ethical considerations regarding privacy rights and potential misuse of sensitive information about individuals' identities. Probing should be conducted ethically with full transparency about its purpose and limitations while safeguarding against unintended consequences such as reinforcing stereotypes or perpetuating discrimination based on protected characteristics like gender identity. Overall, leveraging probes as a tool for understanding gender disparities in ASR systems provides valuable insights but must be approached thoughtfully with careful consideration of ethical implications.

How can fine-grained error metrics provide more informative insights into performance disparities?

Fine-grained error metrics offer detailed insights into performance disparities by providing a nuanced view of model accuracy across different subgroups within a dataset. These metrics go beyond traditional aggregate measures like Word Error Rate (WER) or Character Error Rate (CER) by breaking down errors at a granular level based on specific criteria such as speaker demographics or speech characteristics. By analyzing error rates at this detailed level—such as comparing transcription quality between male-identified speakers versus female-identified speakers—researchers can pinpoint exactly where discrepancies occur within ASR systems. For example: Identifying Discrepancies: Fine-grained error metrics reveal if there are specific segments where one group consistently performs better than another. Quantifying Impact: Researchers can quantify the impact of performance differences by determining thresholds beyond which transcriptions become unusable. Guiding Interventions: Insights from fine-grained error analysis guide interventions aimed at improving system fairness—for instance: balancing training data representation across genders; refining algorithms to address biases; enhancing model robustness against variations in speech patterns. 4 .Enhancing Accountability: Detailed error breakdowns enhance accountability by providing transparent evidence supporting claims about bias mitigation efforts undertaken post-analysis. In summary ,fine-grained error metrics empower researchers with actionable intelligence needed not only understand but also effectively address performance gaps related disparity issues within ASR systems..