Probabilistic Self-Organizing Maps for Text-Independent Speaker Identification

The present paper introduces a novel speaker modeling technique for text-independent speaker identification using probabilistic self-organizing maps (PbSOMs). The basic motivation behind the introduced technique was to combine the self-organizing quality of the self-organizing maps and generative power of Gaussian mixture models. Experimental results show that the introduced modeling technique using probabilistic self-organizing maps significantly outperforms the traditional technique using the classical GMMs and the EM algorithm or its deterministic variant. More precisely, a relative accuracy improvement of roughly 39% has been gained, as well as, a much less sensitivity to the model-parameters initialization has been exhibited by using the introduced speaker modeling technique using probabilistic self-organizing maps

A study of feature extraction for Arabic calligraphy characters recognition

Optical character recognition (OCR) is one of the widely used pattern recognition systems. However, the research on ancient Arabic writing recognition has suffered from a lack of interest for decades, despite the availability of thousands of historical documents. One of the reasons for this lack of interest is the absence of a standard dataset, which is fundamental for building and evaluating an OCR system. In 2022, we published a database of ancient Arabic words as the only public dataset of characters written in Al-Mojawhar Moroccan calligraphy. Therefore, such a database needs to be studied and evaluated. In this paper, we explored the proposed database and investigated the recognition of Al-Mojawhar Arabic characters. We studied feature extraction by using the most popular descriptors used in Arabic OCR. The studied descriptors were associated with different machine learning classifiers to build recognition models and verify their performance. In order to compare the learned and handcrafted features on the proposed dataset, we proposed a deep convolutional neural network for character recognition. Regarding the complexity of the character shapes, the results obtained were very promising, especially by using the convolutional neural network model, which gave the highest accuracy score

Towards an Optimal Speaker Modeling in Speaker Verification Systems using Personalized Background Models

This paper presents a novel speaker modeling approachfor speaker recognition systems. The basic idea of this approach consists of deriving the target speaker model from a personalized background model, composed only of the UBM Gaussian components which are really present in the speech of the target speaker. The motivation behind the derivation of speakers’ models from personalized background models is to exploit the observeddifference insome acoustic-classes between speakers, in order to improve the performance of speaker recognition systems.The proposed approach was evaluatedfor speaker verification task using various amounts of training and testing speech data. The experimental results showed that the proposed approach is efficientin termsof both verification performance and computational cost during the testing phase of the system, compared to the traditional UBM based speaker recognition systems

Enseigner l’« histoire des religions » Que faire de l’Antiquité ?

Enseigner non pas les religions – encore moins « transmettre les croyances religieuses » –, mais leur histoire, en relation avec les contextes, les milieux, les grands événements et personnages, les cultures : donc une histoire à espaces distants et à temporalités différenciées. Sur ce point, l’accord est aujourd’hui à peu près général , mais ce consensus laisse bien des problèmes en suspens, ainsi que le prouve un double débat entre les spécialistes (et les « gens intéressés ») sur la défini..

al-qāmūs l-muḥīṭ: a digital Arabic dictionary: letter yāʾ

Dossier letter yāʾ contains: TXT file: part of plain text corresponding of the section of the letter yāʾ XML files without translation: conversion of text into XML resulting from information extraction and tagging of lemma, part of speech, lexical information, derivational information, and meanings. XML files with translation: enriched with translations of lemmas and corresponding senses using the bilingual dictionary ''An Advanced Learner's Arabic-English Dictionary", by H. Anthony Salmoné and published in 1889. This section contains: 28 chapters, 331 roots and 2778 lexical entrie

Arabic Natural Language Processing: Models, systems and applications

At present, language technologies are instrumental to millions of people, who use them every day with little if any awareness of their existence and role. Popular machine translation systems or web search engines rely more and more on levels of linguistic information automatically overlaid by batch processing tools using language technologies. This development has not only had a huge impact on our daily life, but has also deeply affected the way we think about language as an object of scientific inquiry. The present special issue of JKSU is intended to explore the contribution of computational language models to a better understanding of linguistic, psycholinguistic, sociolinguistic, and literary issues of the Arabic language and culture. The wide array of contributions offered here, ranging from text diacriticisation to psycho-computational modelling of Arabic lexical organisation, bears witness to the maturity of the field and highlight a few general lessons we can learn from current research on Arabic Natural Language Processin

al-qāmūs l-muḥīṭ: a digital Arabic dictionary: letter ṣād

Dossier letter ṣād contains: TXT file: part of plain text corresponding of the section of the letter ṣād XML files without translation: conversion of text into XML resulting from information extraction and tagging of lemma, part of speech, lexical information, derivational information, and meanings. XML files with translation: enriched with translations of lemmas and corresponding senses using the bilingual dictionary ''An Advanced Learner's Arabic-English Dictionary", by H. Anthony Salmoné and published in 1889. This section contains: 21 chapters, 224 roots and 1289 lexical entrie

al-qāmūs l-muḥīṭ: a digital Arabic dictionary: letter lām

Dossier letter lām contains: TXT file: part of plain text corresponding of the section of the letter lām XML files without translation: conversion of text into XML resulting from information extraction and tagging of lemma, part of speech, lexical information, derivational information, and meanings. XML files with translation: enriched with translations of lemmas and corresponding senses using the bilingual dictionary ''An Advanced Learner's Arabic-English Dictionary", by H. Anthony Salmoné and published in 1889. This section contains: 28 chapters, 833 roots and 6400 lexical entrie

al-qāmūs l-muḥīṭ: a digital Arabic dictionary: letter ḫāʾ

Dossier letter ḫāʾ contains: TXT file: part of plain text corresponding of the section of the letter ḫāʾ XML files without translation: conversion of text into XML resulting from information extraction and tagging of lemma, part of speech, lexical information, derivational information, and meanings. XML files with translation: enriched with translations of lemmas and corresponding senses using the bilingual dictionary ''An Advanced Learner's Arabic-English Dictionary", by H. Anthony Salmoné and published in 1889. This section contains: 26 chapters, 209 roots and 883 lexical entrie

al-qāmūs l-muḥīṭ: a digital Arabic dictionary: letter bāʾ

Dossier letter bāʾ contains: TXT file: part of plain text corresponding of the section of the letter bāʾ XML files without translation: conversion of text into XML resulting from information extraction and tagging of lemma, part of speech, lexical information, derivational information, and meanings. XML files with translation: enriched with translations of lemmas and corresponding senses using the bilingual dictionary ''An Advanced Learner's Arabic-English Dictionary", by H. Anthony Salmoné and published in 1889. In addtion, the section bāʾ contains also a folder with mapping: the files of verbs; nouns and adjectives are mapped with PWN and SUMO concepts. This section contains: 28 chapters, 650 roots and 5065 lexical entrie