Large language models (LLM)-based speech synthesis has been widely adopted in
zero-shot speech synthesis. However, they require a large-scale data and
possess the same limitations as previous autoregressive speech models,
including slow inference speed and lack of robustness. This paper proposes
HierSpeech++, a fast and strong zero-shot speech synthesizer for text-to-speech
(TTS) and voice conversion (VC). We verified that hierarchical speech synthesis
frameworks could significantly improve the robustness and expressiveness of the
synthetic speech. Furthermore, we significantly improve the naturalness and
speaker similarity of synthetic speech even in zero-shot speech synthesis
scenarios. For text-to-speech, we adopt the text-to-vec framework, which
generates a self-supervised speech representation and an F0 representation
based on text representations and prosody prompts. Then, HierSpeech++ generates
speech from the generated vector, F0, and voice prompt. We further introduce a
high-efficient speech super-resolution framework from 16 kHz to 48 kHz. The
experimental results demonstrated that the hierarchical variational autoencoder
could be a strong zero-shot speech synthesizer given that it outperforms
LLM-based and diffusion-based models. Moreover, we achieved the first
human-level quality zero-shot speech synthesis. Audio samples and source code
are available at this https URL

HierSpeech++ 是一种快速而强大的零样本语音合成器，适用于文本转语音（TTS）和语音转换（VC），通过采用层次化语音合成框架，显著提高了合成语音的健壮性、表达力和自然度，在零样本语音合成场景中实现了人类级质量。

HierSpeech++：通过分层变分推断在语音的语义和声学表示之间建立联系以进行零样本语音合成

HierSpeech++: Bridging the Gap between Semantic and Acoustic  Representation of Speech by Hierarchical Variational Inference for Zero-shot  Speech Synthesis