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This paper presents a speaking-rate-controllable HiFi-GAN neural vocoder.Original HiFi-GAN is a high-fidelity, computationally efficient, andtiny-footprint neural vocoder. We attempt to incorporate a speaking ratecontrol function into HiFi-GAN for improving the accessibility of syntheticspeech. The proposed method inserts a differentiable interpolation layer intothe HiFi-GAN architecture. A signal resampling method and an image scalingmethod are implemented in the proposed method to warp the mel-spectrograms orhidden features of the neural vocoder. We also design and open-source aJapanese speech corpus containing three kinds of speaking rates to evaluate theproposed speaking rate control method. Experimental results of comprehensiveobjective and subjective evaluations demonstrate that 1) the proposed methodoutperforms a baseline time-scale modification algorithm in speech naturalness,2) warping mel-spectrograms by image scaling obtained the best performanceamong all proposed methods, and 3) the proposed speaking rate control methodcan be incorporated into HiFi-GAN without losing computational efficiency.

Speaking-Rate-Controllable HiFi-GAN Using Feature Interpolation