Dr. Mohamed Lichouri
Institute of Aeronautics and Space Studies, University of Blida, Blida, Algeria
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Representative Publications
View All Publications1 January 2018 in Procedia Computer Science
Journal Article
Mohamed LichouriMAMourad Abbas
Mourad AbbasAFAbed Alhakim Freihat
Abed Alhakim Freihat
DMDhiya El Hak Megtouf
Dhiya El Hak Megtouf
17 March 2019
Book Chapter
KLKhaled Lounnas
Khaled LounnasMAMourad Abbas
Mourad Abbas
HTHocine Teffahi
Hocine Teffahi
Mohamed Lichouri1 April 2020
Conference Paper
KLKhaled Lounnas
Khaled Lounnas
HShassan satori
hassan satori
Mohamed HamidiHTHocine Teffahi
Hocine Teffahi
MAMourad Abbas
Mourad Abbas
Mohamed LichouriIn this paper, we aim to describe a novel technique which is a Combined Automatic Speech Recognition and Language Identification System that uses both ASR and LI technologies which consists of the recognition of spoken digits after identifying their language. An in-house corpus was used mainly for both speech-based multi-lingual identification and speech recognition tasks made of bilingual digits sounds that contains ten digits spoken mainly in two languages: Modern Standard Arabic (MSA) and Amazigh Moroccan dialect. First of all, we develop the Language Identification stage which is the basis of our hybrid system which behaves as front-end of our system that serves for spoken language detection. This facilitates the task of recognition by allocating the output to the appropriate Hidden Model Markov (HMM) based recognition system (Arabic or Amazigh) which improves recognition of a bilingual spoken digit efficiently. For this purpose, a set of parameters were adjusted on our CLIASR system to achieve good results, including classifier parameters, feature vector, and HMM-GMM parameters. The results show that our proposed LI-ASR system performs 33% better than an ordinary ASR for a given bilingual mixed speech corpus.
15 April 2022 in International Journal of Speech Technology
Journal Article
KLKhaled Lounnas
Khaled LounnasMAMourad Abbas
Mourad AbbasMohamed LichouriMohamed HamidiHShassan satori
hassan satoriHTHocine Teffahi
Hocine Teffahi17 September 2022
Conference Paper
In this study, we describe a solution for dealing with the problem of data scarcity in Speech Processing tasks involving low-resource languages, including Automatic Speech Recognition (ASR). This method is based on a set of Data Augmentation (DA) techniques that will be applied to the small corpus that was initially used. This corpus comprises the first 100 Arabic digits uttered by two native Algerians. We used a variety of DA techniques to increase the size of this corpus, including stretching the signal without changing the pitch, simulating an environment using white noise, and finally shifting the sound. Finally, a number of experiments were carried out on two alternative configurations to assess the influence of these strategies on ASR performance. Extensive tests are carried out to verify the impact of the augmented samples in the training set or the training and testing set. Experimental results show that data augmentation plays an important role in improving the accuracy of recognition models, in which the impacts of the data augmentation methods such as Noise, Time Stretch, and rotation are slightly obvious.
2 September 2019
Book Chapter
About Me
Dr. Mohamed Lichouri is an Assistant Professor (Maître de Conférences B) at the Institute of Aeronautics and Space Studies (IASS), Saad Dahleb Blida 1 University, Algeria, and a researcher at the LCPTS laboratory, USTHB, Algiers. He received his PhD in Speech Communication from USTHB in 2022.
His research focuses on speech and natural language processing for Arabic and under-resourced languages, including dialect and spoken language identification, speech corpora, and low-resource ASR. His notable works include word- and sentence-level identification of Algerian and Arabic dialects (Procedia Computer Science), fine-grained dialect identification systems for the MADAR and NADI shared tasks, Arabic podcast and voice-command speech corpora, and a mixed ASR system for Amazigh and Arabic Maghrebi dialects. His recent work applies speech technology to aviation, including noise-robust cockpit command recognition (ICMAE 2026), acoustic drone detection, and pilot–ATC dialogue modeling.
He serves as Scientific Co-Chair of ITAL 2027 and has completed over 38 peer reviews for international journals, including ACM Transactions on Asian and Low-Resource Language Information Processing, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Knowledge-Based Systems, Neural Computing and Applications, and Multimedia Tools and Applications.