no code implementations • ICLR 2020 • Léopold Cambier, Anahita Bhiwandiwalla, Ting Gong, Mehran Nekuii, Oguz H. Elibol, Hanlin Tang
This necessitates increased memory footprint and computational requirements for training.
no code implementations • 30 Sep 2019 • Cory Stephenson, Gokce Keskin, Anil Thomas, Oguz H. Elibol
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.
no code implementations • 10 May 2019 • Oguz H. Elibol, Gokce Keskin, Anil Thomas
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.
no code implementations • 9 May 2019 • Orhan Ocal, Oguz H. Elibol, Gokce Keskin, Cory Stephenson, Anil Thomas, Kannan Ramchandran
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.
no code implementations • 30 Apr 2019 • Gokce Keskin, Tyler Lee, Cory Stephenson, Oguz H. Elibol
We present a Cycle-GAN based many-to-many voice conversion method that can convert between speakers that are not in the training set.
no code implementations • 9 Sep 2016 • Varvara Kollia, Oguz H. Elibol
This paper investigates the use of distributed processing on the problem of emotion recognition from physiological sensors using a popular machine learning library on distributed mode.