DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance

Jinhyeok Yang; Junhyeok Lee; Hyeong-Seok Choi; Seunghun Ji; Hyeongju Kim; Juheon Lee

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arXiv · 2408.14423

DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance

Jinhyeok Yang·Junhyeok Lee·Hyeong-Seok Choi·Seunghun Ji·Hyeongju Kim·Juheon Lee

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Abstract

Text-to-Speech (TTS) models have advanced significantly, aiming to accurately replicate human speech's diversity, including unique speaker identities and linguistic nuances. Despite these advancements, achieving an optimal balance between speaker-fidelity and text-intelligibility remains a challenge, particularly when diverse control demands are considered. Addressing this, we introduce DualSpeech, a TTS model that integrates phoneme-level latent diffusion with dual classifier-free guidance. This approach enables exceptional control over speaker-fidelity and text-intelligibility. Experimental results demonstrate that by utilizing the sophisticated control, DualSpeech surpasses existing state-of-the-art TTS models in performance. Demos are available at https://bit.ly/48Ewoib.

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Jinhyeok Yang, Junhyeok Lee, Hyeong-Seok Choi, Seunghun Ji, Hyeongju Kim, Juheon Lee. 2024-08-26. DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance. https://arxiv.org/abs/2408.14423

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