Before comparing the model with other baselines in TTS and VC tasks, we conducted ablation studies to verify the effectiveness of each component in HierSpeech++.
The zero-shot speech synthesis performance was considerably low, and some studies had to fine-tune or use speaker ID for adaptation, emphasizing the need for robust model architectures.
Ablation studies highlighted the significant performance improvements when applying AMP from BigVGAN, enhancing model metrics across various tasks without compromising F0 consistency.
The objective naturalness of the generated speech improved with the new model, indicating that the balance of loss functions impacts the resulting clarity and authenticity of speech.
#speech-synthesis #machine-learning #model-architecture #voice-conversion #natural-language-processing
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