  Neural network-based Text-to-Speech has significantly improved the quality of
synthesized speech. Prominent methods (e.g., Tacotron2, FastSpeech, FastPitch)
usually generate Mel-spectrogram from text and then synthesize speech using
vocoder (e.g., WaveNet, WaveGlow, HiFiGAN). Compared with traditional
parametric approaches (e.g., STRAIGHT and WORLD), neural vocoder based
end-to-end models suffer from slow inference speed, and the synthesized speech
is usually not robust and lack of controllability. In this work, we propose a
novel updated vocoder, which is a simple signal model to train and easy to
generate waveforms. We use the Gaussian-Markov model toward robust learning of
spectral envelope and wavelet-based statistical signal processing to
characterize and decompose F0 features. It can retain the fine spectral
envelope and achieve high controllability of natural speech. The experimental
results demonstrate that our proposed vocoder achieves better naturalness of
reconstructed speech than the conventional STRAIGHT vocoder, slightly better
than WaveNet, and somewhat worse than the WaveRNN.
