Emmanuel DIDIOT

     Email: didiot@loria.fr

Current Position


I'm now Research Engineer CNRS in ABC team at LORIA since september 2013. I work on M-SVMs applied to protein secondary structure prediction.


Old Positions


·  Expert Engineer INRIA in SYNALP team at LORIA

·  Research Engineer (Postdoc) in the Spoken Language Processing Group at LIMSI-CNRS

·  Project Manager at Hewlett Packard (HP PSF Luxemburg)

·  Research Engineer and Project leader at TNS Media Intelligence (now Kantar Media News Intelligence)

·  ATER at Nancy2 University ,IUT Charlemagne Computer Science Dept.

·  Member of ABC team at LORIA

·  ATER at UHP Nancy1 Computer Science Dept.

·  Member of Speech team at LORIA


Research interests


·  Speech/music discrimination

·  Wavelets in signal processing

·  Pattern recognition (SVMs, neural networks,...)

·  Kernel methods in genomics and computational biology

·  Bioinformatics (computational biology)

·  Model selection for multi-class SVMs

·  And many other things...


Teaching Experience

Nancy2 University ,IUT Charlemagne Nancy2

202 hours -- Lecturer at Computer Science Dept. (2007--2008).
- Object-oriented programming using Java,
- Database administration,
- Computer architecture, 
- Web programming : xHTML, PHP, 
- Relational database management system (normalization, security, ...).


Nancy1 University , Computer Science Dept.

90 hours -- Lecturer at Maths & Computer science Dept. (2006--2007).
- Algorithms and programming for general students,
- Object-oriented programming using Java,
- C programming,
- Database concept,
- Artificial intelligence,
- Network concept and programming.


Nancy2 University ,IUT Charlemagne Nancy2

167 hours -- Lecturer at Computer Science Dept. (2004--2006).
- Object-oriented programming using Java,
- Algorithmic,
- Computer architecture.


List of Publications

A Wavelet-Based Parameterization for Speech/Music Segmentation
Didiot E., Illina I., Fohr D., Mella O.
Journal:  Computer Speech and Language (CSL)
Volume 24, Issue 2, April 2010, Pages 341-357.

A Wavelet-Based Parameterization for Speech/Music Segmentation
Didiot E., Fohr D., Haton J.-P., Illina I., Mella O.
Proceedings of Ninth International Conference on Spoken Language Processing - INTERSPEECH 2006
September 17-21 2006, Pittsburgh, Pennsylvania, USA. pp: 653-656.

Speech/music discrimination based on wavelets for broadcast programs
Didiot E., Fohr D., Haton J.-P., Illina I., Mella O.
Proceedings of International Conference on Signal Processing and Multimedia Applications - SIGMAP 2006
August 7-10 2006, Setubal, Portugal. pp: 151-...

Une nouvelle approche fondée sur les ondelettes pour la discrimination parole/musique
Didiot E., Fohr D., Haton J.-P., Illina I., Mella O.
Proceedings of XXVIes Journées d'Etude sur la Parole - JEP 2006
June 12-16 2006, Dinard, France. pp. 209-212.

Conception et mise en oeuvre de M-SVM dédiées au traitement de séquences biologiques
Didiot E.
Stage de DEA. Rapport de stage.
2003, Nancy, France. 32 pages.


Ph.D. Thesis

Host Laboratory

Speech team at LORIA, France


Jean-Paul Haton (Professor) and Irina Illina (Associate Professor)

Thesis title

Speech/music segmentation for automatic continuous speech transcription

Date of defense

13 Novembre 2007 ( LORIA.)


In this thesis, we study the segmentation of an audio stream in speech, music and speech on music (S/M). This is a fundamental step for all application based on automatic transcription of radiophonic stream and most commonly multimedia. The target application here is a keyword detection system in broadcast programs. The application performance depends on the quality of the signal segmentation given by the speech/music discrimination system. Indeed, bad signal classification can give miss-detections or false alarms. To improve the speech/music discrimination task, we propose a new signal parameterization method. We use the wavelet decomposition which allows an analysis of non-stationary signal like music for instance. We compute different energies on wavelet coefficients to construct our feature vectors. The signal is then segmented in four classes : speech (S), non-speech (NS), music (M) and non-music (NM), thanks to two apart class/non-class classification systems. These classification systems are based on HMM. We chose a class/non-class architecture because it allows to find independently the best parameters for each S/NS and P/NP tasks. A fusion of the classifier ouputs is then performed to obtain the final decision : speech, music or speech on music. The obtained results on a real broadcast program corpus show that our wavelet-based parameterization gives a significant improvement in performance in both M/NM and S/M discrimination tasks compared to the baseline parameterization using cepstral coefficients.