Leveraging the Interplay between Syntactic and Acoustic Cues for Optimizing Korean TTS Pause Formation (2024.lrec-main)
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| Challenge: | despite recent advances in speech synthesis, the focus of research has been on high-resource languages like English. |
| Approach: | They propose a framework that incorporates modeling of syntactic and acoustic cues associated with pausing patterns. |
| Outcome: | The proposed framework generates natural speech even for longer and intricate out-of-domain sentences, despite training on short audio clips. |
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| Challenge: | Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over speech attributes. |
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DNN-based Speech Synthesis Using Abundant Tags of Spontaneous Speech Corpus (2020.lrec-1)
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Yuki Yamashita, Tomoki Koriyama, Yuki Saito, Shinnosuke Takamichi, Yusuke Ijima, Ryo Masumura, Hiroshi Saruwatari
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| Challenge: | Text-to-speech (TTS) models have been developed to generate high-quality speech. |
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| Challenge: | Neural codec language models (or codec LMs) are emerging as a powerful framework for text-to-speech (TTS) despite the close interdependence of codecs and LM, research on codec and lms has largely remained siloed. |
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| Challenge: | Recent advances in generative language modeling applied to discrete speech tokens presented a new avenue for text-to-speech (TTS) synthesis. |
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| Challenge: | Language model prompt optimization research has shown that semantically and grammatically well-formed manually crafted prompts are outperformed by automatically generated token sequences with no apparent meaning or syntactic structure. |
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| Challenge: | Text-to-speech (TTS) systems are limited by limited data and linguistic complexities. |
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| Challenge: | Neural Text-to-Speech systems are a promising approach for high-fidelity speech synthesis . but the efficiency of multi-step sampling in Diffusion Models presents challenges . |
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