Self-Supervised Speech Models Encode Speaker Group Information Differently Based on Training Task
Researchers have found that self-supervised speech recognition models (S3Ms) implicitly encode demographic information about speakers, including gender, age, dialect, ethnicity, and native-speaker status. The study examined models at multiple stages—pretrained, fine-tuned for speaker identification, and fine-tuned for automatic speech recognition (ASR)—finding that different training objectives either amplify or suppress different types of speaker group information. The findings have implications for designing fairer ASR systems, as even fairness-focused algorithms were found to be more effective at reducing phonetically encoded demographic signals than semantically encoded ones.
A study accepted at the Text, Speech, and Dialogue (TSD 2025) conference investigated how self-supervised speech recognition models encode speaker group information (SGI) across different training stages. The researchers found that these models capture demographic attributes—gender, age, dialect, ethnicity, and native-speaker status—and that the nature of this encoding shifts depending on how the model is fine-tuned. Fine-tuning for speaker identification amplified speaker group categories whose variation is primarily phonetic, while fine-tuning for ASR tended to discard phonetically variant SGI but retain semantically variant SGI. Fairness-enhancing ASR algorithms were found to reduce the encoding of phonetically variant demographic information but had limited effect on semantically variant speaker group categories. The study also identified which specific layers and embedding subdimensions are responsible for encoding different demographic attributes, offering a more granular picture of where bias may reside within these models. The authors argue that understanding these encoding patterns is a necessary step toward building ASR systems that perform more equitably across diverse speaker populations.
What's missing
The datasets used for training and evaluation—and their demographic composition—are not described in the abstract, which is relevant for assessing whether the results reflect real-world speaker diversity. It is also unclear whether the identified fairness gaps translate into measurable performance disparities (e.g., word error rate differences) across speaker groups in downstream applications.
What different sources said
- arXiv cs.CLCenter
Speaker Group Encoding in Self-supervised Speech Recognition Models
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