Non-autoregressive real-time Accent Conversion model with voice cloning

Understand

Currently, the development of Foreign Accent Conversion (FAC) models utilizes deep neural network architectures, as well as ensembles of neural networks for speech recognition and speech generation.

• The use of these models is limited by architectural features, which does not allow flexible changes in the timbre of the generated speech and requires the accumulation of context, leading to increased delays in generation and makes these systems unsuitable for use in real-time multi-user communication scenarios.
• We have developed the non-autoregressive model for real-time accent conversion with voice cloning.
• The model generates native-sounding L1 speech with minimal latency based on input L2 accented speech.