In our evaluation, HierSpeech++ consistently outperformed the baseline models in both subjective and objective measures, indicating substantial advancements in voice style transfer performance.
The utilization of a large-scale dataset, including variations from LibriTTS, has been pivotal in enhancing the performance of HierSpeech++ over traditional voice conversion models.
With HierSpeech++, we observe a marked improvement in the naturalness of generated speech, as demonstrated through various ablation studies and comparative evaluations across multiple models.
Our findings support that HierSpeech++ excels in high-fidelity speech synthesis, offering zero-shot voice conversion capabilities that were not previously achieved by existing voice conversion methods.
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