FluentTTS: Text-dependent Fine-grained Style Control for Multi-style TTS
In this paper, we propose a method to flexibly control the local prosodic variation of a neural text-to-speech (TTS) model. To provide expressiveness for synthesized speech, conventional TTS models utilize utterance-wise global style embeddings that are obtained by compressing frame-level embeddings along the time axis. However, since utterance-wise global features do not contain sufficient information to represent the characteristics of word-level local features, they are not appropriate for direct use on controlling prosody at a fine scale. In multi-style TTS models, it is very important to have the capability to control local prosody because it plays a key role in finding the most appropriate text-to-speech pair among many one-to-many mapping candidates.
To explicitly present local prosodic characteristics to the contextual information of the corresponding input text, we propose a module to predict the fundamental frequency (F0) of each text by conditioning on the utterance-wise global style embedding. We also estimate multi-style embeddings using a multi-style encoder, which takes as inputs both a global utterance-wise embedding and a local F0 embedding. Our multi-style embedding enhances the naturalness and expressiveness of synthesized speech and is able to control prosody styles at the word-level or phoneme -level.
|327||International Journal||Jinyoung Lee, Hong-Goo Kang "Two-Stage Refinement of Magnitude and Complex Spectra for Real-Time Speech Enhancement" in IEEE Signal Processing Letters, vol.29, pp.2188-2192, 2022|
|326||International Conference||Hyeon-Kyeong Shin, Hyewon Han, Doyeon Kim, Soo-Whan Chung, Hong-Goo Kang "Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting" in INTERSPEECH (*Best Student Paper Finalist), 2022|
|325||International Conference||Changhwan Kim, Se-yun Um, Hyungchan Yoon, Hong-goo Kang "FluentTTS: Text-dependent Fine-grained Style Control for Multi-style TTS" in INTERSPEECH, 2022|
|324||International Conference||Miseul Kim, Zhenyu Piao, Seyun Um, Ran Lee, Jaemin Joh, Seungshin Lee, Hong-Goo Kang "Light-Weight Speaker Verification with Global Context Information" in INTERSPEECH, 2022|
|323||International Journal||Kyungguen Byun, Se-yun Um, Hong-Goo Kang "Length-Normalized Representation Learning for Speech Signals" in IEEE Access, vol.10, pp.60362-60372, 2022|
|322||International Conference||Doyeon Kim, Hyewon Han, Hyeon-Kyeong Shin, Soo-Whan Chung, Hong-Goo Kang "Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement" in ICASSP, 2022|
|321||International Conference||Chanwoo Lee, Hyungseob Lim, Jihyun Lee, Inseon Jang, Hong-Goo Kang "Progressive Multi-Stage Neural Audio Coding with Guided References" in ICASSP, 2022|
|320||International Conference||Jihyun Lee, Hyungseob Lim, Chanwoo Lee, Inseon Jang, Hong-Goo Kang "Adversarial Audio Synthesis Using a Harmonic-Percussive Discriminator" in ICASSP, 2022|
|319||International Conference||Jinyoung Lee and Hong-Goo Kang "Stacked U-Net with High-level Feature Transfer for Parameter Efficient Speech Enhancement" in APSIPA ASC, 2021|
|318||International Conference||Huu-Kim Nguyen, Kihyuk Jeong, Se-Yun Um, Min-Jae Hwang, Eunwoo Song, Hong-Goo Kang "LiteTTS: A Decoder-free Light-weight Text-to-wave Synthesis Based on Generative Adversarial Networks" in INTERSPEECH, 2021|