H
Hagai Aronowitz
Researcher at IBM
Publications - 73
Citations - 1509
Hagai Aronowitz is an academic researcher from IBM. The author has contributed to research in topics: Speaker recognition & Speaker diarisation. The author has an hindex of 20, co-authored 68 publications receiving 1378 citations. Previous affiliations of Hagai Aronowitz include Bar-Ilan University.
Papers
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Proceedings ArticleDOI
Fast High Dimensional Vector Multiplication Face Recognition
TL;DR: This paper advances descriptor-based face recognition by suggesting a novel usage of descriptors to form an over-complete representation, and by proposing a new metric learning pipeline within the same/not-same framework.
Proceedings ArticleDOI
The RedDots Data Collection for Speaker Recognition
Kong Aik Lee,Anthony Larcher,Guangsen Wang,Patrick Kenny,Niko Brümmer,David A. van Leeuwen,Hagai Aronowitz,Marcel Kockmann,Carlos Vaquero,Bin Ma,Haizhou Li,Themos Stafylakis,Md. Jahangir Alam,Albert Swart,Javier Pérez +14 more
TL;DR: This paper describes data collection efforts conducted as part of the RedDots project which is dedicated to the study of speaker recognition under conditions where test utterances are of short duration and of variable phonetic content.
Proceedings ArticleDOI
A distance measure between GMMs based on the unscented transform and its application to speaker recognition.
Jacob Goldberger,Hagai Aronowitz +1 more
TL;DR: This paper proposes an accurate and efficiently computed approximation of the KL-divergence based on the unscented transform which is usually used to obtain a better alternative to the extended Kalman filter and experimental results indicate that the proposed approximations outperform previously suggested methods.
Proceedings ArticleDOI
Inter dataset variability compensation for speaker recognition
TL;DR: This work analyzes the sources of degradation for a particular setup in the context of an i-vector PLDA system and concludes that the main source for degradation is ani-vector dataset shift, which is introduced using the nuisance attribute projection (NAP) method.
Proceedings ArticleDOI
Audio enhancing with DNN autoencoder for speaker recognition
TL;DR: A DNN-based autoencoder for speech enhancement and its use for speaker recognition systems for distant microphones and noisy data is presented and a more detailed analysis on various conditions of NIST SRE 2010 and PRISM is presented suggesting that the proposed preprocessig is a promising and efficient way to build a robust speaker recognition system.