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Whispered speech differs from phonated speech in acoustic characteristics, which degrades the performance of speaker verification systems in real-life scenarios, including avoiding fully phonated speech to protect privacy, disrupt others, or when the lack of full vocalization is dictated by a disease. In this paper we propose a model with a training recipe to obtain more robust representations against whispered speech hindrances. The proposed system employs an encoder--decoder structure built atop a fine-tuned speaker verification backbone, optimized jointly using cosine similarity--based classification and triplet loss. We gain relative improvement of 22.26\\% compared to the baseline (baseline 6.77\\% vs ours 5.27\\%) in normal vs whispered speech trials, achieving AUC of 98.16\\%. 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We gain relative improvement of 22.26\\% compared to the baseline (baseline 6.77\\% vs ours 5.27\\%) in normal vs whispered speech trials, achieving AUC of 98.16\\%. 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