• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1033-1036
• /
• 2004

The study on speech recognition and understanding has been done for many years. In this paper, we propose a new type of recurrent neural network architecture for speech recognition, in which each output unit is connected to itself and is also fully connected to other output units and all hidden units [1]. Besides that, we also proposed the new architecture and the learning algorithm of recurrent neural network such as Backpropagation Through Time (BPTT, which well-suited. The aim of the study was to observe the difference of Arabic's alphabet like "alif" until "ya". The purpose of this research is to upgrade the people's knowledge and understanding on Arabic's alphabet or word by using Recurrent Neural Network (RNN) and Backpropagation Through Time (BPTT) learning algorithm. 4 speakers (a mixture of male and female) are trained in quiet environment. Neural network is well-known as a technique that has the ability to classified nonlinear problem. Today, lots of researches have been done in applying Neural Network towards the solution of speech recognition [2] such as Arabic. The Arabic language offers a number of challenges for speech recognition [3]. Even through positive results have been obtained from the continuous study, research on minimizing the error rate is still gaining lots attention. This research utilizes Recurrent Neural Network, one of Neural Network technique to observe the difference of alphabet "alif" until "ya".

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.9-16
• /
• 2023

Speech can actively elicit feelings and attitudes by using words. It is important for researchers to identify the emotional content contained in speech signals as well as the sort of emotion that resulted from the speech that was made. In this study, we studied the emotion recognition system using a database in Arabic, especially in the Saudi dialect, the database is from a YouTube channel called Telfaz11, The four emotions that were examined were anger, happiness, sadness, and neutral. In our experiments, we extracted features from audio signals, such as Mel Frequency Cepstral Coefficient (MFCC) and Zero-Crossing Rate (ZCR), then we classified emotions using many classification algorithms such as machine learning algorithms (Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)) and deep learning algorithms such as (Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM)). Our Experiments showed that the MFCC feature extraction method and CNN model obtained the best accuracy result with 95%, proving the effectiveness of this classification system in recognizing Arabic spoken emotions.

• Proceedings of the KSPS conference
• /
• 1996.10a
• /
• pp.75-83
• /
• 1996

The purpose of this paper is to study deletion of the Arabic glottal stop /'/ in Literary Arabic(LA) and Cairene Arabic(CA). Arabic has a diglossia structure - Literary and Colloquial Arabic. The former is the standard and written language, while the latter is the oral language used in dialects of many areas. Of the various Colloquial Arabic dialects the Cairene Arabic is the most influential and powerful dialect, hence it was chosen as the subject study. In this paper the followings are found: (1) The deletion of the Arabic glottal stop /'/ in CA is found much more frequently than that in LA. (2) In a word the deletion is found more frequently in the middle position than in the initial or final position, in which /'/ is sometimes converted weak consonants /w/ or /y/.

• MALSORI
• /
• no.31_32
• /
• pp.83-95
• /
• 1996

The purpose of this paper is to study deletion of the Arabic glottal stop /'/ in Literary Arabic(LA) and Cairene Arabic(CA). Arabic has a diglossia structure - Literary and Colloquial Arabic. The former is the standard and written language, while the latter is the oral language used in dialects of many areas. Of the various Colloquial Arabic dialects the Cairene Arabic is the most influential and powerful dialect, hence it was chosen as the subject study. In this paper the followings are found: (1) The deletion of the Arabic glottal stop /'/ in CA is found much more frequently than that in LA (2) In a word the deletion is found more frequently in the middle position than in the initial or final position, in which /'/ is sometimes converted weak consonants /w/ or /y/.

• Proceedings of the KSPS conference
• /
• 1996.10a
• /
• pp.89-91
• /
• 1996

• Journal of Information Processing Systems
• /
• v.11 no.1
• /
• pp.22-38
• /
• 2015

While Modern Standard Arabic is the formal spoken and written language of the Arab world; dialects are the major communication mode for everyday life. Therefore, identifying a speaker's dialect is critical in the Arabic-speaking world for speech processing tasks, such as automatic speech recognition or identification. In this paper, we examine two approaches that reduce the Universal Background Model (UBM) in the automatic dialect identification system across the five following Arabic Maghreb dialects: Moroccan, Tunisian, and 3 dialects of the western (Oranian), central (Algiersian), and eastern (Constantinian) regions of Algeria. We applied our approaches to the Maghreb dialect detection domain that contains a collection of 10-second utterances and we compared the performance precision gained against the dialect samples from a baseline GMM-UBM system and the ones from our own improved GMM-UBM system that uses a Reduced UBM algorithm. Our experiments show that our approaches significantly improve identification performance over purely acoustic features with an identification rate of 80.49%.

• MALSORI
• /
• no.11_14
• /
• pp.105-120
• /
• 1987

The main purpose of this paper is to examine the rules of phonetic changes in Standard Arabic which keeps the characteristics of phonemes of Proto-Semitic as followings: Assimilation, Dissimilation, Prosthesis, Anaptyxis, Syncope and Contraction, Aphaeresis, and Metathesis.

• Proceedings of the KSPS conference
• /
• 1996.10a
• /
• pp.258-267
• /
• 1996

• Proceedings of the KSPS conference
• /
• 1996.10a
• /
• pp.206-206
• /
• 1996

Previous studies of American English(e.g. Sussman 1991, 1993, 1994) CVC coarticulation with initial consonants representing the labial, alveolar, and velar showed a linear relationship that fits to data points formed by plotting onsets of F2 transition along the y-axis and their corresponding midvowel points along the x-axis. The present study extends the locus equation metric to include the following places of articulation:uvular, pharyngeal, laryngeal, and emphatics. The question of interest is to determine if locus equation could serve as phonetic descriptor for the place of articulation in Arabic. Five male native speakers of Colloquial Egyptian Arabic(CEA) read a list of 204 CVC and CVCC words, containing eight different places of articulation and eight vowels. Average of formant patterns(Fl,F2,F3) onsets, midpoints, and offsets were calculated, using wide band spectrograms obtained by means of the kay spectrograph model(7029), and plotted as locus equations. A summary of the acoustic properties of the place of articulation of CEA will be presented in the frames of bVC and CVb. Strong linear regression relationships were found for every place of articulation.

• Language and Information
• /
• v.8 no.1
• /
• pp.117-136
• /
• 2004

In this paper, we have proposed Auto- TSS, an automatic transcription module of three ambiguous symbols-period (．), colon (:) and slash (／)--using their linguistic contexts. Few previous studies have discussed the problems of ambiguities in reading those symbols into Korean alphabetic letters in order to improve the current Korean TTS (Text-To-Speech) systems. We have classified 9 different reading formulae of the three symbols, analyzed their left and right contexts, and investigated selection rules and distributions between the symbols and their contexts. Based on these linguistic features, 30 stereotyped patterns, 53 rules and 5 heuristics determining the types of reading formulae are investigated for Auto-TSS. This module works modularly in 4 steps. The pilot test was conducted with three test suites, which contain respectively 6,979, 3,491 and 2,450 morpheme clusters containing at least one of three ambiguous symbols and Arabic numeral(s). Encouraging results of 94.3%, 93.0%, 94.2% accuracy were obtained for the test suites. Our next phases are to develop a guessing routine for unknown contexts of the union symbols by using statistical information; to refine the proper nouns and terminology detecting module; and to apply Auto-TSS on a larger scale.