3 citations · 5 across the 2 of their papers we have counts for
4 papers
Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization
Carmel Rabinovitz, Niko Grupen, Aviv Tamar
Robotic tasks such as manipulation with visual inputs require image features that capture the physical properties of the scene, e.g., the position and configuration of objects. Rec…
PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models
Eyal Ben-David, Carmel Rabinovitz, Roi Reichart
Pivot-based neural representation models have lead to significant progress in domain adaptation for NLP. However, previous works that follow this approach utilize only labeled data…
Controllable Sequence-To-Sequence Neural TTS with LPCNET Backend for Real-time Speech Synthesis on CPU
Slava Shechtman, Carmel Rabinovitz, Alex Sorin +2
State-of-the-art sequence-to-sequence acoustic networks, that convert a phonetic sequence to a sequence of spectral features with no explicit prosody prediction, generate speech wi…
High quality, lightweight and adaptable TTS using LPCNet
Zvi Kons, Slava Shechtman, Alex Sorin +2
We present a lightweight adaptable neural TTS system with high quality output. The system is composed of three separate neural network blocks: prosody prediction, acoustic feature…