FIRNet: Fundamental Frequency Controllable Fast Neural Vocoder With Trainable Finite Impulse Response Filter
Yamato Ohtani, Takuma Okamoto, Tomoki Toda, Hisashi Kawai · 2024
Some neural vocoders with fundamental frequency (f0) 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 f0controllability and natural speech quality.