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This paper presents a denoising and dereverberation hierarchical neuralvocoder (DNR-HiNet) to convert noisy and reverberant acoustic features into aclean speech waveform. We implement it mainly by modifying the amplitudespectrum predictor (ASP) in the original HiNet vocoder. This modified denoisingand dereverberation ASP (DNR-ASP) can predict clean log amplitude spectra (LAS)from input degraded acoustic features. To achieve this, the DNR-ASP firstpredicts the noisy and reverberant LAS, noise LAS related to the noiseinformation, and room impulse response related to the reverberation informationthen performs initial denoising and dereverberation. The initial processed LASare then enhanced by another neural network as the final clean LAS. To furtherimprove the quality of the generated clean LAS, we also introduce a bandwidthextension model and frequency resolution extension model in the DNR-ASP. Theexperimental results indicate that the DNR-HiNet vocoder was able to generate adenoised and dereverberated waveform given noisy and reverberant acousticfeatures and outperformed the original HiNet vocoder and a few other neuralvocoders. We also applied the DNR-HiNet vocoder to speech enhancement tasks,and its performance was competitive with several advanced speech enhancementmethods.

Denoising-and-Dereverberation Hierarchical Neural Vocoder for Robust Waveform Generation