In text-to-speech synthesis, the ability to control voice characteristics is vital for various applications. By leveraging thriving text prompt-based generation techniques, it should be possible to enhance the nuanced control of voice characteristics. While previous research has explored the prompt-based manipulation of voice characteristics, most studies have used pre-recorded speech, which limits the diversity of voice characteristics available. Thus, we aim to address this gap by creating a novel corpus and developing a model for prompt-based manipulation of voice characteristics in text-to-speech synthesis, facilitating a broader range of voice characteristics. Specifically, we propose a method to build a sizable corpus pairing voice characteristics descriptions with corresponding speech samples. This involves automatically gathering voice-related speech data from the Internet, ensuring its quality, and manually annotating it using crowdsourcing. We implement this method with Japanese language data and analyze the results to validate its effectiveness. Subsequently, we propose a construction method of the model to retrieve speech from voice characteristics descriptions based on a contrastive learning method. We train the model using not only conservative contrastive learning but also feature prediction learning to predict quantitative speech features corresponding to voice characteristics. We evaluate the model performance via experiments with the corpus we constructed above.

翻译：在文本到语音合成中，控制语音特征的能力对各类应用至关重要。通过利用蓬勃发展的文本提示生成技术，有望增强对语音特征的精细控制。虽然先前研究已探索基于提示的语音特征操控方法，但多数研究采用预录制语音，这限制了可用语音特征的多样性。为此，我们旨在通过创建新型语料库并开发用于文本到语音合成中基于提示的语音特征操控模型来填补这一空白，从而促进更广泛的语音特征范围。具体而言，我们提出一种构建大规模语音特征描述与对应语音样本配对语料库的方法。该方法包括自动从互联网收集语音相关语音数据、确保数据质量，并通过众包进行人工标注。我们使用日语数据实施该方法，并通过分析结果验证其有效性。随后，我们提出一种基于对比学习方法的模型构建方法，该模型能够从语音特征描述中检索语音。我们不仅采用保守的对比学习，还采用特征预测学习来训练模型，以预测与语音特征对应的定量语音特征。通过使用上述构建的语料库进行实验，我们评估了模型性能。