activity
20162024
most citedAutomatic measurement of vowel duration via structured prediction

14 citations · 15 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CL2024

HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing

Arnon Turetzky, Or Tal, Yael Segal-Feldman +9

We present HebDB, a weakly supervised dataset for spoken language processing in the Hebrew language. HebDB offers roughly 2500 hours of natural and spontaneous speech recordings in…

eess.AS20241 cited

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…

cs.SD2024

Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation Network

Yehoshua Dissen, Shiry Yonash, Israel Cohen +1

In the realm of automatic speech recognition (ASR), robustness in noisy environments remains a significant challenge. Recent ASR models, such as Whisper, have shown promise, but th…

eess.AS2024

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…

cs.CL2023

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…

eess.AS2023

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.…