MIKU-PAL: An Automated and Standardized Multi-Modal Method for Speech Paralinguistic and Affect Labeling
This work addresses the problem of costly and inconsistent emotional speech annotation for speech synthesis researchers, offering a cheaper and faster automated solution.
The paper tackles the challenge of acquiring large-scale emotional speech data with strong consistency for speech synthesis by presenting MIKU-PAL, an automated multimodal pipeline that extracts high-consistency emotional speech from unlabeled video data, achieving human-level accuracy (68.5% on MELD) and superior consistency (0.93 Fleiss kappa score).
Acquiring large-scale emotional speech data with strong consistency remains a challenge for speech synthesis. This paper presents MIKU-PAL, a fully automated multimodal pipeline for extracting high-consistency emotional speech from unlabeled video data. Leveraging face detection and tracking algorithms, we developed an automatic emotion analysis system using a multimodal large language model (MLLM). Our results demonstrate that MIKU-PAL can achieve human-level accuracy (68.5% on MELD) and superior consistency (0.93 Fleiss kappa score) while being much cheaper and faster than human annotation. With the high-quality, flexible, and consistent annotation from MIKU-PAL, we can annotate fine-grained speech emotion categories of up to 26 types, validated by human annotators with 83% rationality ratings. Based on our proposed system, we further released a fine-grained emotional speech dataset MIKU-EmoBench(131.2 hours) as a new benchmark for emotional text-to-speech and visual voice cloning.