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Found 5 papers, 2 papers with code
CiwaGAN: Articulatory information exchange
This paper introduces CiwaGAN, a model of human spoken language acquisition that combines unsupervised articulatory modeling with an unsupervised model of information exchange through the auditory modality.
Articulation GAN: Unsupervised modeling of articulatory learning
Articulatory analysis suggests that the network learns to control articulators in a similar manner to humans during speech production.
Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech data
Here, we test how encoding and decoding of lexical semantic information can emerge automatically from raw speech in unsupervised generative deep convolutional networks that combine the production and perception principles of speech.
Interpreting intermediate convolutional layers in unsupervised acoustic word classification
We propose a technique to visualize individual convolutional layers in the classifier that yields highly informative time-series data for each convolutional layer and apply it to unobserved test data.
Interpreting intermediate convolutional layers of generative CNNs trained on waveforms
This technique allows for acoustic analysis of intermediate layers that parallels the acoustic analysis of human speech data: we can extract F0, intensity, duration, formants, and other acoustic properties from intermediate layers in order to test where and how CNNs encode various types of information.
