@inproceedings{50a6fd2b5dab42bda68717d112b6bce0,
title = "NEURAL SPEECH SYNTHESIS ON A SHOESTRING: IMPROVING THE EFFICIENCY OF LPCNET",
abstract = "Neural speech synthesis models can synthesize high quality speech but typically require a high computational complexity to do so. In previous work, we introduced LPCNet, which uses linear prediction to significantly reduce the complexity of neural synthesis. In this work, we further improve the efficiency of LPCNet - targeting both algorithmic and computational improvements - to make it usable on a wide variety of devices. We demonstrate an improvement in synthesis quality while operating 2.5x faster. The resulting open-source LPCNet algorithm can perform real-time neural synthesis on most existing phones and is even usable in some embedded devices.",
keywords = "LPCNet, WaveRNN, neural vocoder",
author = "Valin, {Jean Marc} and Umut Isik and Paris Smaragdis and Arvindh Krishnaswamy",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE; 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 ; Conference date: 22-05-2022 Through 27-05-2022",
year = "2022",
doi = "10.1109/ICASSP43922.2022.9746103",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8437--8441",
booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",
address = "United States",
}