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Fully Differentiable Neural Forced Alignment via Soft Dynamic Programming
Rotem Rousso, Eyal Cohen, Joseph Keshet
Recent advances in sequence modeling have significantly improved ASR systems, bringing them close to human-level recognition accuracy and enhancing robustness across diverse acoust…
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…
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…
Whisper in Medusa's Ear: Multi-head Efficient Decoding for Transformer-based ASR
Yael Segal-Feldman, Aviv Shamsian, Aviv Navon +2
Large transformer-based models have significant potential for speech transcription and translation. Their self-attention mechanisms and parallel processing enable them to capture c…
Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment
Rotem Rousso, Eyal Cohen, Joseph Keshet +1
Forced alignment (FA) plays a key role in speech research through the automatic time alignment of speech signals with corresponding text transcriptions. Despite the move towards en…
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…