FIRNet: Fundamental Frequency Controllable Fast Neural Vocoder With Trainable Finite Impulse Response Filter

Abstract

Some neural vocoders with fundamental frequency (f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) control have succeeded in performing real-time inference on a single CPU while preserving the quality of the synthetic speech. However, compared with legacy vocoders based on signal processing, their inference speeds are still low. This paper proposes a neural vocoder based on the source-filter model with trainable time-variant finite impulse response (FIR) filters, to achieve a similar inference speed to legacy vocoders. In the proposed model, FIRNet, multiple FIR coefficients are predicted using the neural networks, and the speech waveform is then generated by convolving a mixed excitation signal with these FIR coefficients. Experimental results show that FIRNet can achieve an inference speed similar to legacy vocoders while maintaining f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> controllability and natural speech quality.