7 papers
Training-Free Affinity Fusion of Neural and Embedding-Based Speaker Diarization
Yehoshua Dissen, Joseph Keshet, Eduard Golshtein
Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermediate representations are not di…
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…
Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
Gilad Nurko, Roi Benita, Yehoshua Dissen +4
Robust classification in noisy environments remains a fundamental challenge in machine learning. Standard approaches typically treat signal enhancement and classification as separa…
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…
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…
Self-supervised Speaker Diarization
Yehoshua Dissen, Felix Kreuk, Joseph Keshet
Over the last few years, deep learning has grown in popularity for speaker verification, identification, and diarization. Inarguably, a significant part of this success is due to t…
Domain Adaptation For Formant Estimation Using Deep Learning
Yehoshua Dissen, Joseph Keshet, Jacob Goldberger +1
In this paper we present a domain adaptation technique for formant estimation using a deep network. We first train a deep learning network on a small read speech dataset. We then f…