Toward Data-Efficient Speech Synthesis: Active Learning-Based Corpus Construction for Multi-Speaker Text-to-Speech Synthesis

Text-to-speech (TTS) technology is rapidly expanding its applications, including speech-based human-AI interactions. While large-scale data has driven this progress, most previous studies have focused primarily on increasing data volume. However, as vast amounts of web-collected speech data become available, the next challenge is to determine which data is truly beneficial for learning and how to utilize it effectively. This paper proposes an active learning–based corpus construction method for multi-speaker TTS. The proposed framework automatically acquires and selects additional data based on the training and evaluation results of the TTS model, enabling efficient corpus expansion. Experimental results demonstrate that the proposed model-aware corpus construction approach outperforms conventional static sampling and evaluation-in-the-loop methods, achieving consistent improvements in both naturalness and recognition accuracy. Step-wise analysis further reveals that quality filtering and data acquisition contribute to learning improvements in complementary ways. Overall, this study advances TTS corpus construction from a static preparation process to a model-evolving learning process, laying the foundation for data-efficient and self-improving speech synthesis systems.