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arXiv · 2601.14472

Prosody-Guided Harmonic Attention for Phase-Coherent Neural Vocoding in the Complex Spectrum

Mohammed Salah Al-Radhi·Riad Larbi·Mátyás Bartalis·Géza Németh
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Abstract

Neural vocoders are central to speech synthesis; despite their success, most still suffer from limited prosody modeling and inaccurate phase reconstruction. We propose a vocoder that introduces prosody-guided harmonic attention to enhance voiced segment encoding and directly predicts complex spectral components for waveform synthesis via inverse STFT. Unlike mel-spectrogram-based approaches, our design jointly models magnitude and phase, ensuring phase coherence and improved pitch fidelity. To further align with perceptual quality, we adopt a multi-objective training strategy that integrates adversarial, spectral, and phase-aware losses. Experiments on benchmark datasets demonstrate consistent gains over HiFi-GAN and AutoVocoder: F0 RMSE reduced by 22 percent, voiced/unvoiced error lowered by 18 percent, and MOS scores improved by 0.15. These results show that prosody-guided attention combined with direct complex spectrum modeling yields more natural, pitch-accurate, and robust synthetic speech, setting a strong foundation for expressive neural vocoding.

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Mohammed Salah Al-Radhi, Riad Larbi, Mátyás Bartalis, Géza Németh. 2026-01-20. Prosody-Guided Harmonic Attention for Phase-Coherent Neural Vocoding in the Complex Spectrum. https://arxiv.org/abs/2601.14472

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