Zobrazeno 1 - 6 of 6 pro vyhledávání: '"Detai Xin"'
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Akademický článek
JVNV: A Corpus of Japanese Emotional Speech With Verbal Content and Nonverbal Expressions
Autor: Detai Xin, Junfeng Jiang, Shinnosuke Takamichi, Yuki Saito, Akiko Aizawa, Hiroshi Saruwatari
Publikováno v: IEEE Access, Vol 12, Pp 19752-19764 (2024)
We present the JVNV, a Japanese emotional speech corpus with verbal content and nonverbal vocalizations whose scripts are generated by a large-scale language model. Existing emotional speech corpora lack not only proper emotional scripts but also non
Externí odkaz: https://doaj.org/article/b121501764a048fc8ac3fdaf60d2cfd7
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2
Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech
Autor: Dong Yang, Tomoki Koriyama, Yuki Saito, Takaaki Saeki, Detai Xin, Hiroshi Saruwatari
Pause insertion, also known as phrase break prediction and phrasing, is an essential part of TTS systems because proper pauses with natural duration significantly enhance the rhythm and intelligibility of synthetic speech. However, conventional phras
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2cfcd44e1a66851b87c8700e9d7c7213
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3
Mid-attribute speaker generation using optimal-transport-based interpolation of Gaussian mixture models
Autor: Aya Watanabe, Shinnosuke Takamichi, Yuki Saito, Detai Xin, Hiroshi Saruwatari
In this paper, we propose a method for intermediating multiple speakers' attributes and diversifying their voice characteristics in ``speaker generation,'' an emerging task that aims to synthesize a nonexistent speaker's naturally sounding voice. The
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eec0972e1974421c176eed162e5fa2a6
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5
Disentangled Speaker and Language Representations Using Mutual Information Minimization and Domain Adaptation for Cross-Lingual TTS
Autor: Hiroshi Saruwatari, Detai Xin, Shinnosuke Takamichi, Tatsuya Komatsu
Publikováno v: ICASSP
We propose a method for obtaining disentangled speaker and language representations via mutual information minimization and domain adaptation for cross-lingual text-to-speech (TTS) synthesis. The proposed method extracts speaker and language embeddin
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c6b2dab54f235d2beaef34b73d6bfd20
https://doi.org/10.1109/icassp39728.2021.9414226
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