  In our previous work, we proposed a neural vocoder called APNet, which
directly predicts speech amplitude and phase spectra with a 5 ms frame shift in
parallel from the input acoustic features, and then reconstructs the 16 kHz
speech waveform using inverse short-time Fourier transform (ISTFT). APNet
demonstrates the capability to generate synthesized speech of comparable
quality to the HiFi-GAN vocoder but with a considerably improved inference
speed. However, the performance of the APNet vocoder is constrained by the
waveform sampling rate and spectral frame shift, limiting its practicality for
high-quality speech synthesis. Therefore, this paper proposes an improved
iteration of APNet, named APNet2. The proposed APNet2 vocoder adopts ConvNeXt
v2 as the backbone network for amplitude and phase predictions, expecting to
enhance the modeling capability. Additionally, we introduce a multi-resolution
discriminator (MRD) into the GAN-based losses and optimize the form of certain
losses. At a common configuration with a waveform sampling rate of 22.05 kHz
and spectral frame shift of 256 points (i.e., approximately 11.6ms), our
proposed APNet2 vocoder outperformed the original APNet and Vocos vocoders in
terms of synthesized speech quality. The synthesized speech quality of APNet2
is also comparable to that of HiFi-GAN and iSTFTNet, while offering a
significantly faster inference speed.
