用情感一致语音增强小数据，提升儿童故事语音合成自然度
Emotion-Coherent Speech Data Augmentation and Self-Supervised Contrastive Style Training for Enhancing Kids's Story Speech Synthesis
- 通过情感匹配文本合并音频，生成更丰富的表达性语音数据
- 模型生成的句子间停顿分布更接近真实语音，主观评分更高
- 适合需要高质量语音合成的儿童内容生成场景
富有表现力的语音合成需要生动的语调和恰当的停顿。本文提出一种有效策略，通过情感识别器筛选情感一致的文本，合并对应音频以扩充小规模数据集，训练端到端语音合成模型。利用双句音频进行训练，使模型学习到句子间的自然停顿。进一步引入自监督对比学习，优化语音中说话风格嵌入的提取。推理时，模型可一次性生成多句语音，由文本预测的风格引导。实验表明，相比仅使用连续双句音频训练的基线模型，本文方法在句子间停顿分布上更接近真实语音，主观评测显示其自然度和风格适配性均显著提升。
原文摘要 · Abstract (English)
Expressive speech synthesis requires vibrant prosody and well-timed pauses. We propose an effective strategy to augment a small dataset to train an expressive end-to-end Text-to-Speech model. We merge audios of emotionally congruent text using a text emotion recognizer, creating augmented expressive speech data. By training with two-sentence audio, our model learns natural breaks between lines. We further apply self-supervised contrastive training to improve the speaking style embedding extraction from speech. During inference, our model produces multi-sentence speech in one step, guided by the text-predicted speaking style. Evaluations showcase the effectiveness of our proposed approach when compared to a baseline model trained with consecutive two-sentence audio. Our synthesized speeches give a closer inter-sentence pause distribution to the ground truth speech. Subjective evaluations reveal our synthesized speech scored higher in naturalness and style suitability than the baseline.
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