5 papers
Self-Supervised Test-Time Tuning for Packet Loss Concealment
Yehoshua Dissen, Joseph Keshet
Packet loss concealment (PLC) reconstructs audio packets that are missing at the receiver, usually with a trained model whose parameters remain fixed at deployment time. This treat…
How Does a Deep Neural Network Look at Lexical Stress in English Words?
Itai Allouche, Itay Asael, Rotem Rousso +5
Despite their success in speech processing, neural networks often operate as black boxes, prompting the question: what informs their decisions, and how can we interpret them? This…
Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices
Yael Segal-Feldman, Ann R. Bradlow, Matthew Goldrick +1
Open-vocabulary keyword spotting (KWS) refers to the task of detecting words or terms within speech recordings, regardless of whether they were included in the training data. This…
PatchDSU: Uncertainty Modeling for Out of Distribution Generalization in Keyword Spotting
Bronya Roni Chernyak, Yael Segal, Yosi Shrem +1
Deep learning models excel at many tasks but rely on the assumption that training and test data follow the same distribution. This assumption often does not hold in real-world spee…
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…