Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Person

Mathew Magimai Doss

Teaching & PhD PhD Students Karl El Hajal, Tilak Purohit Past EPFL PhD Students as codirector Ramya Rasipuram, Marzieh Razavi, Hannah Muckenhirn, Sandrine Tornay, Subrahmanya Pavankumar Dubagunta, Enno Hermann, Julian David Fritsch, Neha Tarigopula, Mostaani Zohreh, Eklavya Sarkar Courses Automatic speech processing EE-554 The goal of this course is to provide the students with the main formalisms, models and algorithms required for the implementation of advanced speech processing applications (involving, among others, speech coding, speech analysis/synthesis, and speech recognition, speaker recognition). Digital Speech and Audio Coding EE-719 The goal of this course is to introduce the engineering students state-of-the-art speech and audio coding techniques with an emphasis on the integration of knowledge about sound production and auditory perception through signal processing techniques.

Official source

https://people.epfl.ch/mathew.magimaidoss?lang=en

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Official source

https://people.epfl.ch/mathew.magimaidoss?lang=en

About this result

Courses taught by this person (2)

EE-554: Automatic speech processing

The goal of this course is to provide the students with the main formalisms, models and algorithms required for the implementation of advanced speech processing applications (involving, among others, speech coding, speech analysis/synthesis, and speech rec ...

EE-719: Digital Speech and Audio Coding

The goal of this course is to introduce the engineering students state-of-the-art speech and audio coding techniques with an emphasis on the integration of knowledge about sound production and auditory perception through signal processing techniques. ...

Related publications (36)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

People doing similar research (2)

Hervé Bourlard

Jean-Philippe Thiran

Jean-Philippe Thiran was born in Namur, Belgium, in August 1970. He received the Electrical Engineering degree and the PhD degree from the Université catholique de Louvain (UCL), Louvain-la-Neuve, Belgium, in 1993 and 1997, respectively. From 1993 to 1997, he was the co-ordinator of the medical image analysis group of the Communications and Remote Sensing Laboratory at UCL, mainly working on medical image analysis. Dr Jean-Philippe Thiran joined the Signal Processing Institute (ITS) of the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, in February 1998 as a senior lecturer. He was promoted to Assistant Professor in 2004, to Associate Professor in 2011 and is now a Full Professor since 2020. He also holds a 20% position at the Department of Radiology of the University of Lausanne (UNIL) and of the Lausanne University Hospital (CHUV) as Associate Professor ad personam. Dr Thiran's current scientific interests include Computational medical imaging: acquisition, reconstruction and analysis of imaging data, with emphasis on regularized linear inverse problems (compressed sensing, convex optimization). Applications to medical imaging: diffusion MRI, ultrasound imaging, inverse planning in radiotherapy, etc.Computer vision & machine learning: image and video analysis, with application to facial expression recognition, eye tracking, lip reading, industrial inspection, medical image analysis, etc.

Mathew Magimai Doss

Graph Chatbot

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

Using auxiliary sources of knowledge for automatic speech recognition

Utterance Verification-Based Dysarthric Speech Intelligibility Assessment Using Phonetic Posterior Features