164 citations · 320 across the 8 of their papers we have counts for
12 papers
Fairness in the Eyes of the Data: Certifying Machine-Learning Models
Shahar Segal, Yossi Adi, Benny Pinkas +3
We present a framework that allows to certify the fairness degree of a model based on an interactive and privacy-preserving test. The framework verifies any trained model, regardle…
Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation
Felix Kreuk, Joseph Keshet, Yossi Adi
We propose a self-supervised representation learning model for the task of unsupervised phoneme boundary detection. The model is a convolutional neural network that operates direct…
Phoneme Boundary Detection using Learnable Segmental Features
Felix Kreuk, Yaniv Sheena, Joseph Keshet +1
Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc. In this…
Hide and Speak: Towards Deep Neural Networks for Speech Steganography
Felix Kreuk, Yossi Adi, Bhiksha Raj +2
Steganography is the science of hiding a secret message within an ordinary public message, which is referred to as Carrier. Traditionally, digital signal processing techniques, suc…
Out-of-Distribution Detection using Multiple Semantic Label Representations
Gabi Shalev, Yossi Adi, Joseph Keshet
Deep Neural Networks are powerful models that attained remarkable results on a variety of tasks. These models are shown to be extremely efficient when training and test data are dr…
Fooling End-to-end Speaker Verification by Adversarial Examples
Felix Kreuk, Yossi Adi, Moustapha Cisse +1
Automatic speaker verification systems are increasingly used as the primary means to authenticate costumers. Recently, it has been proposed to train speaker verification systems us…
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse +2
Deep Neural Networks have recently gained lots of success after enabling several breakthroughs in notoriously challenging problems. Training these networks is computationally expen…
Houdini: Fooling Deep Structured Prediction Models
Moustapha Cisse, Yossi Adi, Natalia Neverova +1
Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines. So far, most existing methods only work for classification and…
Automatic Measurement of Pre-aspiration
Yaniv Sheena, Míša Hejná, Yossi Adi +1
Pre-aspiration is defined as the period of glottal friction occurring in sequences of vocalic/consonantal sonorants and phonetically voiceless obstruents. We propose two machine le…
Learning Similarity Functions for Pronunciation Variations
Einat Naaman, Yossi Adi, Joseph Keshet
A significant source of errors in Automatic Speech Recognition (ASR) systems is due to pronunciation variations which occur in spontaneous and conversational speech. Usually ASR sy…
Automatic measurement of vowel duration via structured prediction
Yossi Adi, Joseph Keshet, Emily Cibelli +3
A key barrier to making phonetic studies scalable and replicable is the need to rely on subjective, manual annotation. To help meet this challenge, a machine learning algorithm was…
Sequence Segmentation Using Joint RNN and Structured Prediction Models
Yossi Adi, Joseph Keshet, Emily Cibelli +1
We describe and analyze a simple and effective algorithm for sequence segmentation applied to speech processing tasks. We propose a neural architecture that is composed of two modu…