In this work, we study disentanglement between speaker and environment by combining an adversarial framework with contrastive learning objectives. We investigate supervised contrastive learning (SupCon), which exploits environment labels to structure the environment subspace, and self-supervised SimCLR, which learns invariance from augmented views. Experiments on a controlled synthetic dataset (ST1) and a more realistic corpus (CML-TTS) show that SupCon yields the most discriminative and stable speaker embeddings on ST1, achieving the best verification performance (EER=4.70%, MinDCF=0.24). Overall, our findings emphasize (i) the importance of synthetic benchmarks for diagnosing disentanglement under controlled factor variation and (ii) the effectiveness of combining contrastive and adversarial objectives to encourage speaker representations that are both discriminative and less sensitive to environmental factors.