9 citations · 22 across the 6 of their papers we have counts for
4 papers · 1 filter
CiwaGAN: Articulatory information exchange
Gašper Beguš, Thomas Lu, Alan Zhou +2
Humans encode information into sounds by controlling articulators and decode information from sounds using the auditory apparatus. This paper introduces CiwaGAN, a model of human s…
Articulation GAN: Unsupervised modeling of articulatory learning
Gašper Beguš, Alan Zhou, Peter Wu +1
Generative deep neural networks are widely used for speech synthesis, but most existing models directly generate waveforms or spectral outputs. Humans, however, produce speech by c…
Interpreting intermediate convolutional layers in unsupervised acoustic word classification
Gašper Beguš, Alan Zhou
Understanding how deep convolutional neural networks classify data has been subject to extensive research. This paper proposes a technique to visualize and interpret intermediate l…
Interpreting intermediate convolutional layers of generative CNNs trained on waveforms
Gašper Beguš, Alan Zhou
This paper presents a technique to interpret and visualize intermediate layers in generative CNNs trained on raw speech data in an unsupervised manner. We argue that averaging over…