Joseph Keshet

Joseph ‘Yossi’ Keshet

Speech, Language, and Artificial Intelligence.

I am an Associate Professor at the Viterbi Faculty of Electrical and Computer Engineering at the Technion – Israel Institute of Technology, where I direct the Speech, Language, and AI Lab, affiliated with the Signal and Image Processing Lab (SIPL). I am also a Courtesy Faculty Member with the Faculty of Data and Decision Sciences.

Full CV

My Research

I am passionate about human speech—one of the most fundamental yet intricately complex signals we know. Speech carries not only the information a speaker intends to convey but also reveals their identity, emotional state, and even medical condition. My drive to understand and quantify speech shapes my research interests, which span both machine learning and the computational study of human speech and language.

Blog

News

Workshop
iSpeech 2025
On Sept 17, 2025, we will hold the second Israeli conference on Speech & AI at the Technion. The program and registration can be found here.  
Interview
The New Supercomputer “Israel-1” by NVIDIA
An interview in YNet (Yedioth Ahronoth) was published with me on Friday in “7 Days” by Dror Feuer on the topic of the new supercomputer “Israel-1” by NVIDIA.
Interview
Why Amazon Scholar Yossi Keshet remains ‘excited about speech’
A blog post at Amazon Science by Larry Hardesty.
Committee
IEEE/ACM Transactions on Audio, Speech, and Language Processing
I was nominated as an Associated Editor for the journal IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Publications

Transformer architectures, similar to those behind ChatGPT, are the foundation of today’s most successful speech recognition systems. Likewise, diffusion-based models have become key to state-of-the-art speech synthesis and generation. My recent publications focus on improving the performance and conceptual understanding of these architectures within the speech domain.
Sound

All publications

How Does a Deep Neural Network Look at Lexical Stress?

Itai Allouche, Itay Asael, Rotem Rousso, Vered Dassa, Ann Bradlow, Seung-Eun Kim, Matthew Goldrick, Joseph Keshet

The Journal of the Acoustical Society of America,  Vol. 159, Issue 2, pp. 1348-1358, 2026.
Large Language Model Guided Decoding for Self-Supervised Speech Recognition

Eyal Cohen, Bhiksha Raj, Joseph Keshet

Preprint, 2026
Beyond Transcription: Mechanistic Interpretability in ASR

Neta Glazer, Yael Segal-Feldman, Hilit Segev, Aviv Shamsian, Asaf Buchnick, Gill Hetz, Ethan Fetaya, Joseph Keshet, Aviv Navon

The 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26), 2026
DRAX: Speech Recognition with Discrete Flow Matching

Aviv Navon, Aviv Shamsian, Neta Glazer, Yael Segal-Feldman, Gill Hetz, Joseph Keshet, Ethan Fetaya

Preprint, 2025.
Spectral Analysis of Diffusion Models with Application to Schedule Design

Roi Benita, Michael Elad, Joseph Keshet

The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
CarelessWhisper: Turning Whisper into a Causal Streaming Model

Tomer Krichli, Bhiksha Raj, Joseph Keshet

Preprint, 2025.

Courses

Speech Processing with Deep Learning

Speech recognition and synthesis have become foundational components in the development of modern artificial intelligence, bridging the gap between human communication and machine interaction. This course aims to provide a comprehensive introduction to the core principles of speech signal processing and modeling, essential for understanding how machines perceive and generate human language.

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Deep Learning

This course covers key theoretical and practical tools for deep learning, with an emphasis on supervised learning. Topics include gradient descent, fully-connected and convolutional networks, training methods, and architectures for sequential data like transformers. We also explore techniques to improve data and resource efficiency, such as pre-training, self-supervised learning, quantization, and pruning.

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Transformers and Large Language Models

Transformers are powerful deep learning architectures that have revolutionized how machines understand and generate sequential data. From enabling breakthroughs in language models like ChatGPT to powering cutting-edge systems in speech recognition and computer vision, Transformers have become a cornerstone of modern AI. This course explores their design, capabilities, and impact across a range of domains.

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|ECE 460747 / DDS 970201|ECE 460217|ECE 0480011