GrainSpeech: Less Context, More Detail for Compact Speech Synthesis
Compact acoustic models face a challenging quality-capacity trade-off. We investigate two factors in this regime: encoder context and Mel-spectrogram supervision. A receptive-field-scaling study shows that expanding self-attention beyond 15 phonemes provides no consistent gains in pitch, energy, or duration prediction. Guided by this finding, we introduce a fixed-receptive-field convolutional encoder that reduces the respective prediction errors by 36.0%, 17.3%, and 3.4%. We further show that directly transferring image-domain gradient-variance supervision restores fine-scale variation but degrades predicted quality, motivating a Mel-specific formulation with axis-specific gradients, over...
| Authors | Zitao Liang, Chang Gao |
|---|---|
| Primary category | eess.AS |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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