情感一致的语音数据增强与自监督对比式风格训练以提升儿童故事语音合成
Emotion-Coherent Speech Data Augmentation and Self-Supervised Contrastive Style Training for Enhancing Kids's Story Speech Synthesis
AI总结：
本文提出通过情感一致的语音数据增强和自监督对比训练，提升儿童故事语音合成的表达性和自然度。
摘要：
表达性语音合成需要生动的语调和恰当的停顿。我们提出了一种有效的策略来增强一个小数据集，以训练一个表达性的端到端文本到语音模型。我们使用文本情感识别器合并情感一致的音频，生成增强的表达性语音数据。通过使用双句音频进行训练，我们的模型学习到自然的行间停顿。我们进一步应用自监督对比训练来提高从语音中提取说话风格嵌入的能力。在推理过程中，我们的模型能够一步生成多句语音，由文本预测的说话风格引导。评估显示，与使用连续两句话音频训练的基线模型相比，我们提出的方法效果更佳。我们的合成语音在句子间停顿分布上更接近真实语音。主观评估显示，我们的合成语音在自然度和风格适配性方面得分高于基线。
英文摘要：
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.
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。
