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20212026
most citedInterpreting intermediate convolutional layers of generative CNNs trained on waveforms

9 citations · 22 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Syntactic Belief Update as the Driver of Garden Path Processing Difficulty

Alan Zhou, Miloš Stanojević, John T. Hale

Garden path sentences present a processing difficulty for humans--- the sentence prefix leads the listener towards one interpretation, until the listener hears a critical word that…

cs.SD2023

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…

cs.SD2022★ 4 cited

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…

cs.CL2022★ 4 cited

Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech data

Gašper Beguš, Alan Zhou

Human speakers encode information into raw speech which is then decoded by the listeners. This complex relationship between encoding (production) and decoding (perception) is often…

cs.SD2021★ 5 cited

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…

cs.SD2021★ 9 cited

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…