Search Results for author:
Found 6 papers, 1 papers with code
Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning
We present a novel approach which uses prompt-tuning to control the extraction rates of memorized content in LLMs.
Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework
We conclude that this improvement in ASC performance comes from the regularization effect of using AET and not from the network's improved ability to discern between acoustic events.
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.
Canary Extraction in Natural Language Understanding Models
In this work, we present a version of such an attack by extracting canaries inserted in NLU training data.
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.
Ranked #1 on
Adversarial Attack
on WSJ0-2mix
