  This paper presents a reverberation module for source-filter-based neural
vocoders that improves the performance of reverberant effect modeling. This
module uses the output waveform of neural vocoders as an input and produces a
reverberant waveform by convolving the input with a room impulse response
(RIR). We propose two approaches to parameterizing and estimating the RIR. The
first approach assumes a global time-invariant (GTI) RIR and directly learns
the values of the RIR on a training dataset. The second approach assumes an
utterance-level time-variant (UTV) RIR, which is invariant within one utterance
but varies across utterances, and uses another neural network to predict the
RIR values. We add the proposed reverberation module to the phase spectrum
predictor (PSP) of a HiNet vocoder and jointly train the model. Experimental
results demonstrate that the proposed module was helpful for modeling the
reverberation effect and improving the perceived quality of generated
reverberant speech. The UTV-RIR was shown to be more robust than the GTI-RIR to
unknown reverberation conditions and achieved a perceptually better
reverberation effect.
