Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet

• URL: http://arxiv.org/abs/2202.11169v1
• Date: Tue, 22 Feb 2022 20:42:00 GMT
• Title: Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet
• Authors: Jean-Marc Valin, Umut Isik, Paris Smaragdis, Arvindh Krishnaswamy
• Abstract summary: We improve the efficiency of LPCNet 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.
• Score: 35.44634252321666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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