Papers

StylebookTTS: Zero-Shot Text-to-Speech Leveraging Unsupervised Style Representation

International Conference
2021~
작성자
임형섭
작성일
2024-10-21 17:22
조회
298
Authors : Juhwan Yoon, Hyungseob Lim, Hyeonjin Cha, Hong-Goo Kang

Year : 2024

Publisher / Conference : APSIPA ASC

Research area : Speech Signal Processing, Text-to-Speech

Presentation : Poster

Zero-shot text-to-speech (ZS-TTS) is a TTS system capable of generating speech in voices it has not been explicitly trained on. While many recent ZS-TTS models effectively capture target speech styles using a single global style feature per speaker, they still face challenges in achieving high speaker similarity for voices that were not previously encountered. In this study, we propose StylebookTTS, a novel ZS-TTS framework that extracts and utilizes multiple target style embeddings based on the content. We begin by extracting style information from target speeches, leveraging linguistic content obtained through a self-supervised learning (SSL) model. The extracted style information is stored in a collection of embeddings called a stylebook, which represents styles in an unsupervised manner without the need for text transcriptions or speaker labeling. Simultaneously, the input text is transformed into content features using a transformer-based text-to-unit module, which links the text to the SSL representations of an utterance reading that text. The final target style is created by selecting embeddings from the stylebook that most closely align with the content features generated from the text. Finally, a diffusion-based decoder is employed to synthesize the mel-spectrogram by combining the final target style with the content features generated from the text. Experimental results demonstrate that StylebookTTS achieves greater speaker similarity compared to baseline models, while also being highly data-efficient, requiring significantly less paired text-audio data.
전체 367
35 International Conference Juhwan Yoon, Hyungseob Lim, Hyeonjin Cha, Hong-Goo Kang "StylebookTTS: Zero-Shot Text-to-Speech Leveraging Unsupervised Style Representation" in APSIPA ASC, 2024
34 International Conference Doyeon Kim, Yanjue Song, Nilesh Madhu, Hong-Goo Kang "Enhancing Neural Speech Embeddings for Generative Speech Models" in APSIPA ASC, 2024
33 International Conference Miseul Kim, Soo-Whan Chung, Youna Ji, Hong-Goo Kang, Min-Seok Choi "Speak in the Scene: Diffusion-based Acoustic Scene Transfer toward Immersive Speech Generation" in INTERSPEECH, 2024
32 International Conference Woo-Jin Chung, Hong-Goo Kang "Speaker-Independent Acoustic-to-Articulatory Inversion through Multi-Channel Attention Discriminator" in INTERSPEECH, 2024
31 International Conference Juhwan Yoon, Woo Seok Ko, Seyun Um, Sungwoong Hwang, Soojoong Hwang, Changhwan Kim, Hong-Goo Kang "UNIQUE : Unsupervised Network for Integrated Speech Quality Evaluation" in INTERSPEECH, 2024
30 International Conference Yanjue Song, Doyeon Kim, Hong-Goo Kang, Nilesh Madhu "Spectrum-aware neural vocoder based on self-supervised learning for speech enhancement" in EUSIPCO, 2024
29 International Conference Hyewon Han, Naveen Kumar "A cross-talk robust multichannel VAD model for multiparty agent interactions trained using synthetic re-recordings" in Hands-free Speech Communication and Microphone Arrays (HSCMA, Satellite workshop in ICASSP), 2024
28 International Conference Yanjue Song, Doyeon Kim, Nilesh Madhu, Hong-Goo Kang "On the Disentanglement and Robustness of Self-Supervised Speech Representations" in International Conference on Electronics, Information, and Communication (ICEIC) (*awarded Best Paper), 2024
27 International Conference Yeona Hong, Miseul Kim, Woo-Jin Chung, Hong-Goo Kang "Contextual Learning for Missing Speech Automatic Speech Recognition" in International Conference on Electronics, Information, and Communication (ICEIC), 2024
26 International Conference Juhwan Yoon, Seyun Um, Woo-Jin Chung, Hong-Goo Kang "SC-ERM: Speaker-Centric Learning for Speech Emotion Recognition" in International Conference on Electronics, Information, and Communication (ICEIC), 2024