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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…
WhisperRT -- Turning Whisper into a Causal Streaming Model
Tomer Krichli, Bhiksha Raj, Joseph Keshet
Automatic Speech Recognition (ASR) has seen remarkable progress, with models like OpenAI Whisper and NVIDIA Canary achieving state-of-the-art (SOTA) performance in offline transcri…
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…
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…
Spectral Analysis of Diffusion Models with Application to Schedule Design
Roi Benita, Michael Elad, Joseph Keshet
Diffusion models (DMs) have emerged as powerful tools for modeling complex data distributions and generating realistic new samples. Over the years, advanced architectures and sampl…