Summary of Fairssd: Understanding Bias in Synthetic Speech Detectors, by Amit Kumar Singh Yadav et al.
FairSSD: Understanding Bias in Synthetic Speech Detectors
by Amit Kumar Singh Yadav, Kratika Bhagtani, Davide Salvi, Paolo Bestagini, Edward J.Delp
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed synthetic speech detectors can effectively identify genuine speech, but recent incidents have shown that these methods are being misused to commit fraud. To combat this misuse, researchers have developed various detection methods, some of which provide better interpretability and generalization capabilities. However, there is a lack of understanding regarding the bias in these detectors, particularly with regards to gender, age, and accent. This study examines the existing synthetic speech detectors for potential bias towards specific demographics and whether they may unfairly misclassify speech from individuals with impairments. The experiments conducted on six popular detectors using over 0.9 million audio signals reveal that most detectors exhibit gender, age, and accent bias, emphasizing the need for future work to ensure fairness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Some synthetic speech methods can create speech that sounds just like a human. Recently, these methods have been misused to cheat or deceive people. To stop this misuse, researchers have created tools to detect when speech is not real. Some of these detection tools are better than others at explaining their decisions and working well in real-world situations. However, no one knows how biased these detectors might be towards certain groups of people, like men versus women, young people versus old, or those with different accents. This study looks at six popular synthetic speech detection methods to see if they have biases against specific demographics and whether they unfairly misclassify the speech of people who are impaired in some way. |
Keywords
» Artificial intelligence » Generalization
