Speaker identification in novels is crucial for speech synthesis systems to assign appropriate voices in audiobook production. It attributes a speaker to an utterance through context analysis. Traditional approaches heavily rely on human-annotated datasets, which are costly and scarce, limiting model performance. To overcome this, we propose a simple-yet-effective data augmentation method using large language models (LLMs) to generate synthetic dialogues and post-process the dialogues into augmented training instances. Our experiments show that this method achieves a state-of-the-art accuracy of 82.6%, surpassing the previous baseline by 2.4%. Performance gains are especially notable in the Implicit (hard) category, where our method exceeds the previous baseline by 3.5%. Our analysis suggests that it enhances the ability to capture long-term dependencies and there is a mutually reinforce effect between the Implicit and Anaphoric (middle) categories.