Puffin: pitch-synchronous neural waveform generation for fullband speech on modest devices

Abstract

We present a neural vocoder designed with low-powered Alternative and Augmentative Communication devices in mind. By combining elements of successful modern vocoders with established ideas from an older generation of technology, our system is able to produce high quality synthetic speech at 48kHz on devices where neural vocoders are otherwise prohibitively complex. The system is trained adversarially using differentiable pitch synchronous overlap add, and reduces complexity by relying on pitch synchronous Inverse Short-Time Fourier Transform (ISTFT) to generate speech samples. Our system achieves comparable quality with a strong (HiFi-GAN) baseline while using only a fraction of the compute. We present results of a perceptual evaluation as well as an analysis of system complexity.

ICASSP 2023

References in corpus (5)

• WaveNet: A Generative Model for Raw Audio
• MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
• SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
• Parallel WaveNet: Fast High-Fidelity Speech Synthesis
• Autovocoder: Fast Waveform Generation from a Learned Speech Representation using Differentiable Digital Signal Processing

Cited by in corpus (1)

• Autovocoder: Fast Waveform Generation from a Learned Speech Representation using Differentiable Digital Signal Processing