1 citations · 1 across the 13 of their papers we have counts for
13 papers
Stress Testing Unlearning Algorithms
Noam Diamant, Neta Glazer, Ethan Fetaya
Recently, machine unlearning, the removal of specific training data influence from a model, has gained increasing attention. In large language models (LLMs), unlearning is particul…
Are Audio-Language Models Listening? Audio-Specialist Heads for Adaptive Audio Steering
Neta Glazer, Lenny Aharon, Ethan Fetaya
Multimodal large language models can exhibit text dominance, over-relying on linguistic priors instead of grounding predictions in non-text inputs. One example is large audio-langu…
Questioning the Stability of Visual Question Answering
Amir Rosenfeld, Neta Glazer, Ethan Fetaya
Visual Language Models (VLMs) have achieved remarkable progress, yet their reliability under small, meaning-preserving input changes remains poorly understood. We present the first…
Drax: Speech Recognition with Discrete Flow Matching
Aviv Navon, Aviv Shamsian, Neta Glazer +4
Diffusion and flow-based non-autoregressive (NAR) models have shown strong promise in large language modeling, however, their potential for automatic speech recognition (ASR) remai…
Beyond Transcription: Mechanistic Interpretability in ASR
Neta Glazer, Yael Segal-Feldman, Hilit Segev +6
Interpretability methods have recently gained significant attention, particularly in the context of large language models, enabling insights into linguistic representations, error…
UmbraTTS: Adapting Text-to-Speech to Environmental Contexts with Flow Matching
Neta Glazer, Aviv Navon, Yael Segal +6
Recent advances in Text-to-Speech (TTS) have enabled highly natural speech synthesis, yet integrating speech with complex background environments remains challenging. We introduce…
Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
Neta Glazer, David Chernin, Idan Achituve +2
Recent advancements in Text-to-Speech (TTS) models, particularly in voice cloning, have intensified the demand for adaptable and efficient deepfake detection methods. As TTS system…
FlowTSE: Target Speaker Extraction with Flow Matching
Aviv Navon, Aviv Shamsian, Yael Segal-Feldman +3
Target speaker extraction (TSE) aims to isolate a specific speaker's speech from a mixture using speaker enrollment as a reference. While most existing approaches are discriminativ…
Keyword-Guided Adaptation of Automatic Speech Recognition
Aviv Shamsian, Aviv Navon, Neta Glazer +2
Automatic Speech Recognition (ASR) technology has made significant progress in recent years, providing accurate transcription across various domains. However, some challenges remai…
Multi Task Inverse Reinforcement Learning for Common Sense Reward
Neta Glazer, Aviv Navon, Aviv Shamsian +1
One of the challenges in applying reinforcement learning in a complex real-world environment lies in providing the agent with a sufficiently detailed reward function. Any misalignm…
Combining Language Models For Specialized Domains: A Colorful Approach
Daniel Eitan, Menachem Pirchi, Neta Glazer +7
General purpose language models (LMs) encounter difficulties when processing domain-specific jargon and terminology, which are frequently utilized in specialized fields such as med…
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.…
Auxiliary Learning as an Asymmetric Bargaining Game
Aviv Shamsian, Aviv Navon, Neta Glazer +3
Auxiliary learning is an effective method for enhancing the generalization capabilities of trained models, particularly when dealing with small datasets. However, this approach may…