Affectron：基于情感和语境对齐的非语言语音生成
Affectron: Emotional Speech Synthesis with Affective and Contextually Aligned Nonverbal Vocalizations
AI总结：
Affectron通过引入非语言语音增强训练策略和结构掩码技术，提升情感语音合成中非语言语音的多样性与自然性，同时保持语音流的自然性。
摘要：
非语言语音（NVs），如笑声和叹息，是情感语音合成中表达情感线索的核心。然而，在开放环境中，学习多样且语境对齐的NVs仍具有挑战性，因为NV数据有限且缺乏显式监督。受此挑战启发，我们提出Affectron作为情感和语境对齐NV生成的框架。基于一个小规模的开放和解耦语料库，Affectron引入了NV增强的训练策略，扩展了NV类型的分布和插入位置。我们进一步将NV结构掩码融入一个在纯语音上预训练的语音骨干网络中，以实现多样且自然的NV合成。实验结果表明，Affectron产生的NV比基线系统更具表现力和多样性，同时保持语音流的自然性。
英文摘要：
Nonverbal vocalizations (NVs), such as laughter and sighs, are central to the expression of affective cues in emotional speech synthesis. However, learning diverse and contextually aligned NVs remains challenging in open settings due to limited NV data and the lack of explicit supervision. Motivated by this challenge, we propose Affectron as a framework for affective and contextually aligned NV generation. Built on a small-scale open and decoupled corpus, Affectron introduces an NV-augmented training strategy that expands the distribution of NV types and insertion locations. We further incorporate NV structural masking into a speech backbone pre-trained on purely verbal speech to enable diverse and natural NV synthesis. Experimental results demonstrate that Affectron produces more expressive and diverse NVs than baseline systems while preserving the naturalness of the verbal speech stream.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
