零 shot TTS 中的语音印象控制
Voice Impression Control in Zero-Shot TTS
AI总结：
本文提出了一种基于大型语言模型的零 shot TTS 语音印象控制方法，通过低维向量实现语音印象的自动调节与生成。
摘要：
语音中的参数/非语言信息在塑造听众印象方面至关重要。尽管零 shot 文本到语音（TTS）已经实现了高说话者保真度，但调节细微的参数/非语言信息以控制感知的语音特征，即印象，仍然具有挑战性。为此，我们开发了一种零 shot TTS 中的语音印象控制方法，该方法利用低维向量来表示各种语音印象对（例如，深浅）。客观和主观评估的结果都证明了我们方法在印象控制上的有效性。此外，通过大型语言模型生成此向量，可以从自然语言描述的期望印象生成目标印象，从而消除了手动优化的需要。音频示例可在我们的演示页面上获得（https://ntt-hilab-gensp.github.io/is2025voiceimpression/）。
英文摘要：
Para-/non-linguistic information in speech is pivotal in shaping the listeners' impression. Although zero-shot text-to-speech (TTS) has achieved high speaker fidelity, modulating subtle para-/non-linguistic information to control perceived voice characteristics, i.e., impressions, remains challenging. We have therefore developed a voice impression control method in zero-shot TTS that utilizes a low-dimensional vector to represent the intensities of various voice impression pairs (e.g., dark-bright). The results of both objective and subjective evaluations have demonstrated our method's effectiveness in impression control. Furthermore, generating this vector via a large language model enables target-impression generation from a natural language description of the desired impression, thus eliminating the need for manual optimization. Audio examples are available on our demo page (https://ntt-hilab-gensp.github.io/is2025voiceimpression/).
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。
