Generative De-Quantization for Neural Speech Codec Via Latent Diffusion-Reference-Cited by-同舟云学术

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Generative De-Quantization for Neural Speech Codec Via Latent Diffusion

Published:2024-04-14 Issue: Volume:33 Page:1251-1255
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Yang Haici¹,Jang Inseon²,Kim Minje³

Affiliation:

1. Indiana University,Department of Intelligent Systems Engineering,Bloomington,IN,USA,47408

2. Electronics and Telecommunications Research Institute,Daejeon,South Korea,34129

3. University of Illinois at Urbana-Champaign,Department of Computer Science,IL,USA,61801

Funder

Electronics and Telecommunications Research Institute

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446556.pdf?arnumber=10446556

Reference29 articles.

1. AudioLM: A Language Modeling Approach to Audio Generation

2. Neural codec language models are zero-shot text to speech synthesizers;Wang,2023

3. Audiogen: Textually guided audio generation;Kreuk,2022

4. SoundStream: An End-to-End Neural Audio Codec

5. High-fidelity audio compression with improved RVQGAN;Kumar,2023

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