Bunched LPCNet : Vocoder for Low-cost Neural Text-To-Speech Systems
By Ravichander Vipperla, Sangjun Park, Kihyun Choo, Samin S. Ishtiaq, Kyoungbo Min, Sourav Bhattacharya, Abhinav Mehrotra, Alberto Gil C. P. Ramos, Nicholas Donald Lane. 2020
27 citations recorded by the source corpus.
LPCNet is an efficient vocoder that combines linear prediction and deep neural network modules to keep the computational complexity low. In this work, we present two techniques to further reduce it's complexity, aiming for a low-cost LPCNet vocoder-based neural Text-to-Speech (TTS) System. These techniques are: 1) Sample-bunching, which allows LPCNet to generate more than one audio sample per inference; and 2) Bit-bunching, which reduces the computations in the final layer of LPCNet. With the proposed bunching techniques, LPCNet, in conjunction with a Deep Convolutional TTS (DCTTS) acoustic model, shows a 2.19x improvement over the baseline run-time when running on a mobile device, with a less than 0.1 decrease in TTS mean opinion score (MOS).
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Xu Tan
- Tao Qin
- Tie-Yan Liu
- Frank K. Soong
- Nikolaos Ellinas
- Georgios Vamvoukakis
- Konstantinos Markopoulos
- Georgia Maniati
Citations in the corpus
- A Survey on Neural Speech Synthesis (2021, background): “Some following works further improve LPCNet from different perspectives, such as reducing complexity for speedup [364, 269, 148], and improving stability for better quality [126].”
- High Quality Streaming Speech Synthesis with Low, Sentence-Length-Independent Latency (2020, methodology): “We use the LPCNet [18] vocoder as adapted for reduced complexity by the parallel work of [22].”