Search Results for author:
Found 3 papers, 0 papers with code
Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion
Due to the use of a single encoder, our method can generalize to converting the voice of out-of-training speakers to speakers in the training dataset.
Semi-supervised and Population Based Training for Voice Commands Recognition
We present a rapid design methodology that combines automated hyper-parameter tuning with semi-supervised training to build highly accurate and robust models for voice commands classification.
Semi-supervised voice conversion with amortized variational inference
In this work we introduce a semi-supervised approach to the voice conversion problem, in which speech from a source speaker is converted into speech of a target speaker.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
