English(EN) Rethinking Speaker Embeddings for Speech Generation: Sub-Center Modeling for Capturing Intra-Speaker Diversity

新的子中心建模增强了语音生成的变异性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员为语音生成中的说话人嵌入开发了一个新的子中心建模框架。该方法摒弃了单一原型表示，学习多个子中心以更好地捕捉说话人内部的多样性。该方法旨在通过保留对生成至关重要的变异性，同时仍保持强大的说话人验证性能，来提高生成语音的自然度和表现力。 AI

影响这项研究通过更好地捕捉人类声音的细微差别，有望带来更自然、更具表现力的 AI 生成语音。

排序理由该集群包含一篇详细介绍语音生成新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv cs.LG TIER_1 English(EN) · Ismail Rasim Ulgen, John H. L. Hansen, Carlos Busso, Berrak Sisman · 2026-08-31 04:00

重新思考语音生成中的说话人嵌入：子中心建模用于捕捉说话人内部多样性

arXiv:2407.04291v4 Announce Type: replace-cross Abstract: Modeling speech variation is key to natural, expressive generation. Speaker embeddings are commonly used to condition personalized speech systems, but they are typically trained for speaker recognition, where intra-speaker…