FlowVocoder: A small Footprint Neural Vocoder based Normalizing flow for Speech Synthesis

Understand

Recently, autoregressive neural vocoders have provided remarkable performance in generating high-fidelity speech and have been able to produce synthetic speech in real-time.

• However, autoregressive neural vocoders such as WaveFlow are capable of modeling waveform signals from mel-spectrogram, its number of parameters is significant to deploy on edge devices.
• Though NanoFlow, which has a small number of parameters, is a state-of-the-art autoregressive neural vocoder, the performance of NanoFlow is marginally lower than WaveFlow.
• Therefore, we propose a new type of autoregressive neural vocoder called FlowVocoder, which has a small memory footprint and is capable of generating high-fidelity audio in real-time.