A thread pool processes prosodic phrases asynchronously, enabling earlier audio playback while maintaining speech synthesis quality.
A speech model combines speaker, language, and text features to simplify training for low-data TTS and voice conversion.
An audio corpus yields channel impulse responses that lower augmentation cost and improve recognition for noisy, low-resource recordings.
Separate encoders isolate speaker identity from phonetic content for efficient, private speech reconstruction at low bitrates.
Frequency subframes and synchronous adjacent-sample prediction reduce vocoder loops for faster, more efficient audio synthesis.
A supervised decoder combines text with user voice features to generate diverse speech styles from the same learning data.