Conducting Ablation Studies to Verify the Effectiveness of Each Component in HierSpeech++ | HackerNoonHierSpeech++ leverages advanced architecture improvements for enhanced zero-shot voice synthesis and voice conversion capabilities.
How We Used the LibriTTS Dataset to Train the Hierarchical Speech Synthesizer | HackerNoonThe paper discusses training a hierarchical speech synthesizer using the LibriTTS dataset, emphasizing the importance of data diversity for robust voice style transfer.
The 7 Objective Metrics We Conducted for the Reconstruction and Resynthesis Tasks | HackerNoonThe 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.
Zero-shot Text-to-Speech: How Does the Performance of HierSpeech++ Fare With Other Baselines? | HackerNoonHierSpeech++ is a leading zero-shot text-to-speech model that excels in naturalness and overall performance.
HierSpeech++: How Does It Compare to Vall-E, Natural Speech 2, and StyleTTS2? | HackerNoonThe 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.
The Limitations of HierSpeech++ and a Quick Fix | HackerNoonThe model enhances zero-shot speech synthesis but faces challenges with background noise and speech clarity.
Conducting Ablation Studies to Verify the Effectiveness of Each Component in HierSpeech++ | HackerNoonHierSpeech++ leverages advanced architecture improvements for enhanced zero-shot voice synthesis and voice conversion capabilities.
How We Used the LibriTTS Dataset to Train the Hierarchical Speech Synthesizer | HackerNoonThe paper discusses training a hierarchical speech synthesizer using the LibriTTS dataset, emphasizing the importance of data diversity for robust voice style transfer.
The 7 Objective Metrics We Conducted for the Reconstruction and Resynthesis Tasks | HackerNoonThe 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.
Zero-shot Text-to-Speech: How Does the Performance of HierSpeech++ Fare With Other Baselines? | HackerNoonHierSpeech++ is a leading zero-shot text-to-speech model that excels in naturalness and overall performance.
HierSpeech++: How Does It Compare to Vall-E, Natural Speech 2, and StyleTTS2? | HackerNoonThe 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.
The Limitations of HierSpeech++ and a Quick Fix | HackerNoonThe model enhances zero-shot speech synthesis but faces challenges with background noise and speech clarity.
AI detection tools for audio deepfakes fall short. How 4 tools fare and what we can do instead - PoynterAI-generated audio used in robocalls led to FCC banChallenges in detecting AI-generated audio clipDeepfake audio is easier and cheaper to produce than video