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This work proposes replacing an empty representation with a learnable unconditional embedding, optimized to represent a meaningful unconditional state, and shows that learnable null embeddings consistently outperform fixed null embeddings across speaker similarity, speech stability, and expressiveness, while exhibiting greater robustness to larger guidance scales.