Contrastive and Adversarial Disentanglement for Speaker Representations in Brazilian Portuguese

2026-04-12 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Speech Processing · Depth: Expert, quick

Summary

Researchers investigated disentanglement between speaker and environment factors in Brazilian Portuguese speech by integrating adversarial frameworks with contrastive learning. The study explored both supervised contrastive learning (SupCon), which leverages environment labels to structure the environment subspace, and self-supervised SimCLR, designed to learn invariance from augmented data views. Experiments were conducted on two datasets: a controlled synthetic dataset (ST1) and a more realistic corpus (CML-TTS). Results indicated that SupCon produced the most discriminative and stable speaker embeddings on the ST1 dataset, achieving an Equal Error Rate (EER) of 4.70% and a Minimum Detection Cost Function (MinDCF) of 0.24 for speaker verification. The findings highlight the utility of synthetic benchmarks for diagnosing disentanglement and the efficacy of combining contrastive and adversarial objectives.

Key takeaway

For research scientists developing robust speaker verification systems, integrating supervised contrastive learning with adversarial objectives can significantly improve speaker embedding stability and discriminability. You should consider using controlled synthetic datasets to effectively diagnose and fine-tune disentanglement performance, ensuring your models are less sensitive to environmental variations in real-world applications.

Key insights

Combining adversarial and contrastive learning improves speaker representation disentanglement from environmental factors.

Principles

• Synthetic benchmarks aid disentanglement diagnosis.
• SupCon yields stable, discriminative speaker embeddings.

Method

The method combines an adversarial framework with supervised contrastive learning (SupCon) and self-supervised SimCLR objectives to disentangle speaker and environment factors in speech representations.

In practice

• Use SupCon for robust speaker verification.
• Employ synthetic data for disentanglement diagnostics.

Topics

• Speaker Representations
• Disentanglement Learning
• Contrastive Learning
• Adversarial Frameworks
• Supervised Contrastive Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.