Postfilters to Modify the Modulation Spectrum for Statistical Parametric Speech Synthesis

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WaveNet: A Generative Model for Raw Audio (2016, background): “…developing high-quality vocoders (Kawahara et al., 1999; Agiomyrgiannakis, 2015; Morise et al., 2016), improving the accuracy of generative models (Zen et al., 2007; 2013; Fan et al., 2014; Uria et al., 2015), and compensating the oversmoothing effect (Toda & Tokuda, 2007; Takamichi et al., 2016).”
A Survey on Neural Speech Synthesis (2021, background): “Modeling the one-to-many mapping under the regular L1 loss [83, 354] without enough input information will cause over-smoothing mel-spectrogram prediction [347, 328], e,g.”
A Neural Parametric Singing Synthesizer (2017, background): “A recently proposed metric, the Modulation Spectrum (MS) [40], allows visualizing the spectral content of predicted time sequences.”
Adaptive Wavenet Vocoder for Residual Compensation in GAN-Based Voice Conversion (2018, background): “Global variance (GV) [3] and modulation spectrum (MS) [18], among others [19, 20], are typical examples to address the problem.”
Utterance Selection Techniques for TTS Systems Using Found Speech (2016, methodology): “Moreover, using the Modulation Spectrum as a postfilter has shown gains in synthesis [17].”
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks (2017, background): “Global variance (GV) [6] and modulation spectrum (MS) [12] are well-known examples for reproducing natural statistics.”