#neural-models

#neural-models

The paper discusses training a hierarchical speech synthesizer using the LibriTTS dataset, emphasizing the importance of data diversity for robust voice style transfer.

The article explores advanced speech synthesis tasks using various metrics for evaluation, focusing on voice conversion and text-to-speech models.

It details the experimentation and methodologies applied in evaluating speech synthesis quality.

The Hierspeech++ model outperforms existing models in naturalness and prompt similarity for zero-shot speech synthesis.

The evaluation revealed important limitations in similarity with ground truth versus prompt-generated speech.

Style Prompt Replication (SPR) enables effective synthesis from short speech prompts, enhancing style transfer in speech generation.

HierSpeech++ demonstrates superior performance in voice style transfer compared to traditional models, significantly enhancing naturalness in speech synthesis.

SpeechSR improves speech super-resolution by upsampling from 16 kHz to 48 kHz with superior performance and efficiency over existing models.