The main purpose of the present research is to support Arabic Text- to - Speech synthesizers, with
natural prosody, based on linguistic analysis of texts to synthesize, and automatic prosody generation,
using rules which are deduced from recorded s
ignals analysis, of different types of sentences in Arabic. All
the types of Arabic sentences (declarative and constructive) were enumerated with the help of an expert in
Arabic linguistics . A textual corpus of about 2500 sentences covering most of these types was built and
recorded both in natural prosody and without prosody. Later, these sentences were analyzed to extract
prosody effect on the signal parameters, and to build prosody generation rules. In this paper, we present
the results on negation sentences, applied on synthesized speech using the open source tool MBROLA. The
results can be used with any parametric Arabic synthesizer. Future work will apply the rules on a new
Arabic synthesizer based on semi-syllables units, which is under development in the Higher Institute for
Applied Sciences and Technology.
In the present work, we present our Arabic Semi-Syllable Synthesizer. The work consists of seven steps: (1) building a Semi-Syllable Speech Database for Arabic Semi-Syllable Synthesizer, (2) building the Natural Language Processing Module which compr
ises a Text Pre-processing Module and a Text to Phoneme conversion using Arabic Transcription from Orthographic to Phonemes, (3) followed by a Phoneme to Semi-Syllables Mapping using a Syllabification Expert System, (4) an Acoustic Word Stress Analysis for Continuous Arabic Speech based on the three prosodic parameters (fundamental frequency, intensity, duration) in order to detect stressed syllables.
A Mobile Ad hoc Network (MANET) is a network of wireless mobile devices deployed without the aid of any pre-existing infrastructure or centralized administration.
In this project we study wavelet and wavelet transform, and the possibility of its employment in the processing and analysis of the speech signal in order to enhance the signal and remove noise of it. We will present different algorithms that depend
on the wavelet transform and the mechanism to apply them in order to get rid of noise in the speech, and compare the results of the application of these algorithms with some traditional algorithms that are used to enhance the speech.
في هذا المشروع سوف نستثمر مجموعظة من الأدوات الرياضية من خوارزميات تعلم الآلة machine learning و الأمثلة المحدبة convex optimization و "النماذج الاحتمالية البيانية" probabilistic graphical model في إطار "الشبكات المعرفية" cognitive networking وذلك
لأمثلة optimize أنواع مختلفة من الشبكات اللاسلكية مثل: شبكات الحساسات اللاسلكية WSN ، و الشبكات التكتيكية الهجينة
tactical networks ، و الشبكات المحلية اللاسلكية WLAN . تتمثل "الشبكات المعرفية" في تطبيق "معرفة"
cognition على كامل مكدس البروتوكولات protocol stack لتحقيق أهداف الأداء، بخلاف "الراديو المعرفي"
cognitive radio الذي يطبق المعرفة فقط على الطبقة الفيزيائية.
