  This paper proposes a novel bidirectional neural vocoder, named BiVocoder,
capable both of feature extraction and reverse waveform generation within the
short-time Fourier transform (STFT) domain. For feature extraction, the
BiVocoder takes amplitude and phase spectra derived from STFT as inputs,
transforms them into long-frame-shift and low-dimensional features through
convolutional neural networks. The extracted features are demonstrated suitable
for direct prediction by acoustic models, supporting its application in
text-to-speech (TTS) task. For waveform generation, the BiVocoder restores
amplitude and phase spectra from the features by a symmetric network, followed
by inverse STFT to reconstruct the speech waveform. Experimental results show
that our proposed BiVocoder achieves better performance compared to some
baseline vocoders, by comprehensively considering both synthesized speech
quality and inference speed for both analysis-synthesis and TTS tasks.
