164 citations · 430 across the 25 of their papers we have counts for
31 papers
Open-vocabulary Keyword-spotting with Adaptive Instance Normalization
Aviv Navon, Aviv Shamsian, Neta Glazer +2
Open vocabulary keyword spotting is a crucial and challenging task in automatic speech recognition (ASR) that focuses on detecting user-defined keywords within a spoken utterance.…
A Baseline for Detecting Out-of-Distribution Examples in Image Captioning
Gabi Shalev, Gal-Lev Shalev, Joseph Keshet
Image captioning research achieved breakthroughs in recent years by developing neural models that can generate diverse and high-quality descriptions for images drawn from the same…
DDKtor: Automatic Diadochokinetic Speech Analysis
Yael Segal, Kasia Hitczenko, Matthew Goldrick +3
Diadochokinetic speech tasks (DDK), in which participants repeatedly produce syllables, are commonly used as part of the assessment of speech motor impairments. These studies rely…
Formant Estimation and Tracking using Probabilistic Heat-Maps
Yosi Shrem, Felix Kreuk, Joseph Keshet
Formants are the spectral maxima that result from acoustic resonances of the human vocal tract, and their accurate estimation is among the most fundamental speech processing proble…
THOR: Threshold-Based Ranking Loss for Ordinal Regression
Tzeviya Sylvia Fuchs, Joseph Keshet
In this work, we present a regression-based ordinal regression algorithm for supervised classification of instances into ordinal categories. In contrast to previous methods, in thi…
Unsupervised Word Segmentation using K Nearest Neighbors
Tzeviya Sylvia Fuchs, Yedid Hoshen, Joseph Keshet
In this paper, we propose an unsupervised kNN-based approach for word segmentation in speech utterances. Our method relies on self-supervised pre-trained speech representations, an…
Self-supervised Speaker Diarization
Yehoshua Dissen, Felix Kreuk, Joseph Keshet
Over the last few years, deep learning has grown in popularity for speaker verification, identification, and diarization. Inarguably, a significant part of this success is due to t…
Correcting Mispronunciations in Speech using Spectrogram Inpainting
Talia Ben-Simon, Felix Kreuk, Faten Awwad +2
Learning a new language involves constantly comparing speech productions with reference productions from the environment. Early in speech acquisition, children make articulatory ad…
DeepFry: Identifying Vocal Fry Using Deep Neural Networks
Bronya R. Chernyak, Talia Ben Simon, Yael Segal +4
Vocal fry or creaky voice refers to a voice quality characterized by irregular glottal opening and low pitch. It occurs in diverse languages and is prevalent in American English, w…
CNN-based Spoken Term Detection and Localization without Dynamic Programming
Tzeviya Sylvia Fuchs, Yael Segal, Joseph Keshet
In this paper, we propose a spoken term detection algorithm for simultaneous prediction and localization of in-vocabulary and out-of-vocabulary terms within an audio segment. The p…
Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy
Bronya Roni Chernyak, Bhiksha Raj, Tamir Hazan +1
This paper proposes an attack-independent (non-adversarial training) technique for improving adversarial robustness of neural network models, with minimal loss of standard accuracy…
Redesigning the classification layer by randomizing the class representation vectors
Gabi Shalev, Gal-Lev Shalev, Joseph Keshet
Neural image classification models typically consist of two components. The first is an image encoder, which is responsible for encoding a given raw image into a representative vec…
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…
Dr.VOT : Measuring Positive and Negative Voice Onset Time in the Wild
Yosi Shrem, Matthew Goldrick, Joseph Keshet
Voice Onset Time (VOT), a key measurement of speech for basic research and applied medical studies, is the time between the onset of a stop burst and the onset of voicing. When the…
SpeechYOLO: Detection and Localization of Speech Objects
Yael Segal, Tzeviya Sylvia Fuchs, Joseph Keshet
In this paper, we propose to apply object detection methods from the vision domain on the speech recognition domain, by treating audio fragments as objects. More specifically, we p…
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…
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
Felix Kreuk, Assi Barak, Shir Aviv-Reuven +3
In recent years, deep learning has shown performance breakthroughs in many applications, such as image detection, image segmentation, pose estimation, and speech recognition. Howev…
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…