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Found 5 papers, 0 papers with code
Learning to Compute the Articulatory Representations of Speech with the MIRRORNET
Most organisms including humans function by coordinating and integrating sensory signals with motor actions to survive and accomplish desired tasks.
An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models
Knowing that harmonicity is a critical cue for these networks to group sources, in this work, we perform a thorough investigation on ConvTasnet and DPT-Net to analyze how they perform a harmonic analysis of the input mixture.
Acoustic To Articulatory Speech Inversion Using Multi-Resolution Spectro-Temporal Representations Of Speech Signals
We used U. of Wisconsin X-ray Microbeam (XRMB) database of clean speech signals to train a feed-forward deep neural network (DNN) to estimate articulatory trajectories of six tract variables.
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems
We evaluate their performance with mixtures of natural speech versus slightly manipulated inharmonic speech, where harmonics are slightly frequency jittered.
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The Mirrornet : Learning Audio Synthesizer Controls Inspired by Sensorimotor Interaction
Experiments to understand the sensorimotor neural interactions in the human cortical speech system support the existence of a bidirectional flow of interactions between the auditory and motor regions.
