Joseph (Yossi) Keshet

Department of Computer Science
Bar Ilan University,
Room 44, Bldg 109
Ramat Gan, 52900, Israel

Email: jkeshet at

Tel: +972-3-738-4378

I am a faculty member in the Department of Computer Science at Bar-Ilan University. My research interests concern both machine learning and computational study of human speech and language. In machine learning my research is focused on deep learning and structured prediction, while my research on speech and language is focused on speech processing, speech recognition, acoustic phonetics, and pathological speech.

My technological goal is to improve the state-of-the-art in applications such as automatic speech recognition, speech indexing and retrieval, acoustic scene analysis, and language understanding. My scientific goal is to contribute to research in human speech communication, phonetics, and medical speech pathology using data-driven methods. I believe that exploiting the structure of language and designing theoretically well-founded statistical machine learning algorithms for particular tasks that are able to make use of large datasets, can solve the complex problems involved in speech and language research. To a great extent, my research interests focus on interdisciplinary areas combining the fields of speech science, machine learning, and linguistics. I therefore constantly collaborate with colleagues from those fields.


Speech, Language and Deep Learning Lab

The research in the lab is focused on statistical and machine learning techniques applied to the modeling and processing of speech and language. A typical problem in speech and language processing has a very large number of training examples, is sequential, highly structured, and has a unique measure of performance. The lab's goal is to develop rigorous statistical and machine learning algorithms that maximize performance by matching the internal structure of the problem and by optimizing its unique measure of performance.

Yossi Adi
Ph.D. candidate
Deep learning, structured prediction, speech and audio processing

Einat Naaman
M.Sc. candidate
Pronunciation modelling for conversational speech using deep learning

Amir Gottlieb
M.Sc. candidate
Automatic analysis of Doppler echocardiograms

Tzeviya Fuchs
M.Sc. candidate
Spoken term detection with an automatically adjusted threshold

Shua Dissen
M.Sc. candidate
Formant estimation and tracking using deep learning

Felix Kreuk
M.Sc. candidate
New loss function in training deep networks

Yaniv Sheena
M.Sc. candidate
Incroporating structure prediction with deep learning

Tal Bokobza
M.Sc. candidate
Real-time lip syncing

Gabi Shalev
M.Sc. candidate
Models for speech pathologies

Resources and Code

The lab is commited to reproducible results. The GitHub repository gives you access to our code, tools and information on how to setup and use. {Deep} Phonetic Tools is a project done in collaboration with Matt Goldrick and Emily Ciballi, where we proposed a set of phonetic tools for measureing VOT, voswel duration, word duration and formants, and are all based on deep learning.


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