# Bunched LPCNet : Vocoder for Low-cost Neural Text-To-Speech Systems

arXiv: https://arxiv.org/abs/2008.04574 · HTML: https://mlsanity.com/paper/2008.04574/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [High Quality Streaming Speech Synthesis with Low, Sentence-Length-Independent Latency](https://mlsanity.com/paper/2111.09052/) (2021)
- [SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis](https://mlsanity.com/paper/2204.03040/) (2022)
- [Prosodic Clustering for Phoneme-level Prosody Control in End-to-End Speech Synthesis](https://mlsanity.com/paper/2111.10177/) (2021)
- [Iterative autoregression: a novel trick to improve your low-latency speech enhancement model](https://mlsanity.com/paper/2211.01751/) (2022)
- [Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Control](https://mlsanity.com/paper/2111.10168/) (2021)
- [Controllable speech synthesis by learning discrete phoneme-level prosodic representations](https://mlsanity.com/paper/2211.16307/) (2022)
- [How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures](https://mlsanity.com/paper/2106.15021/) (2021)
- [Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control](https://mlsanity.com/paper/2111.09146/) (2021)
- [Into-TTS : Intonation Template Based Prosody Control System](https://mlsanity.com/paper/2204.01271/) (2022)
- [Read the Room: Adapting a Robot's Voice to Ambient and Social Contexts](https://mlsanity.com/paper/2205.04952/) (2022)
- [Framewise WaveGAN: High Speed Adversarial Vocoder in Time Domain with Very Low Computational Complexity](https://mlsanity.com/paper/2212.04532/) (2022)
- [Time out of Mind: Generating Rate of Speech conditioned on emotion and speaker](https://mlsanity.com/paper/2301.12331/) (2023)
- [Triple M: A Practical Text-to-speech Synthesis System With Multi-guidance Attention And Multi-band Multi-time LPCNet](https://mlsanity.com/paper/2102.00247/) (2021)
- [Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet](https://mlsanity.com/paper/2202.11169/) (2022)
- [Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge](https://mlsanity.com/paper/2203.14416/) (2022)

## Abstract (arXiv)

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).