Arabic Phonemes Research Articles

Acoustical Comparison between /u/ and /u:/ Arabic Vowels for Non-Native Speakers

<p>The articulation of Arabic phonemes is essential for the Malay community since Arabic language is mandatory to perform worship. Hence, in this paper, an acoustical analysis of Arabic phonemes for vowels /u/ and /u:/ is discussed based on tokens pronounced by Malay speakers. The experimental results showed that the Malay speakers are inclined to utter these Arabic phonemes similar to the native speakers and it was also found from the analysis that the vowel /u/ and /u: was articulated as high-back vowels. Conversely, the vowel /u/ was located lower than /u:/ as in the vowel-space. Alternatively results also showed that /u/ and /u:/ is higher than the other vowels specifically /a/ and /a:/. In addition, the statistical analysis showed that the formant frequencies of both short and long dummah for formant frequency F1, F2 and F3 have more variation in terms of /u/ as compare to /u:/. In contrast formant frequency F4 and F5 are more diversity in terms of /u:/.</p>

Acoustical Comparison between /u/ and /u:/ Arabic Vowels for Non-native Speakers

The articulation of Arabic phonemes is essential for the Malay community since Arabic language is mandatory to perform worship. Hence, in this paper, an acoustical analysis of Arabic phonemes for vowels /u/ and /u:/ is discussed based on tokens pronounced by Malay speakers. The experimental results showed that the Malay speakers are inclined to utter these Arabic phonemes similar to the native speakers and it was also found from the analysis that the vowel /u/ and /u: was articulated as high-back vowels. Conversely, the vowel /u/ was located lower than /u:/ as in the vowel-space. Alternatively results also showed that /u/ and /u:/ is higher than the other vowels specifically /a/ and /a:/. In addition, the statistical analysis showed that the formant frequencies of both short and long dummah for formant frequency F1, F2 and F3 have more variation in terms of /u/ as compare to /u:/. In contrast formant frequency F4 and F5 are more diversity in terms of /u:/.

A Dataset for Speech Recognition to Support Arabic Phoneme Pronunciation

A Dataset for Speech Recognition to Support Arabic Phoneme Pronunciation

NEURAL FUZZY PETRI NET BASED ARABIC PHONEME CLASSIFIER WITH MFCC FEATURE EXTRACTION

In this paper Arabic phoneme classification is employed using Mel Frequency Cepstral Coefficient (MFCC) as the basic recognition features. These features are first calculated, then used as an input to fuzzy neural Petri net. One network are used for each phoneme. The network was first trained based on a set of training recoded data, then the network are validated based on another set of data. Classification accuracy were then calculated and it have been found that the resulting total accuracy reached 74.94%.

Helpful Statistics in Recognizing Basic Arabic Phonemes

The recognition of continuous speech is one of the main challenges in the building of automatic speech recognition (ASR) systems, especially when it comes to phonetically complex languages such as Arabic. An ASR system seems to be actually in a blocked alley. Nearly all solutions follow the same general model. The previous research focused on enhancing its performance by incorporating supplementary features. This paper is part of ongoing research efforts aimed at developing a high-performance Arabic speech recognition system for learning and teaching purposes. It investigates a statistical analysis of certain distinctive features of the basic Arabic phonemes which seems helpful in enhancing the performance of a baseline HMM-based ASR system. The statistics are collected using a particular Arabic speech database, which involves ten different male speakers and more than eight hours of speech which covers all Arabic phonemes. In HMM modeling framework, the statistics provided are helpful in establishing the appropriate number of HMM states for each phoneme and they can also be utilized as an initial condition for the EM estimation procedure, which generally, accelerates the estimation process and, thus, improves the performance of the system. The obtained findings are presented and possible applications of automatic speech recognition and speaker identification systems are also suggested.

The Computation of Assimilation of Arabic Language Phonemes

The computational phonology is fairly a new science that deals with studying phonological rules under the computation point of view. Computational phonology is based on the phonological rules, which are the processes that are applied to phonemes to produce another phoneme under specific phonetic environment. A type of these phonological processes is the assimilation process, which its rules reform the involved phonemes regarding the place of articulation, the manner of articulation, and/or voicing. Thus, assimilation is considered as a consequence of phonological coarticulation. Arabic, like other natural languages, has systematic phonemes’ changing rules. This paper aims to automate the assimilation rules of the Arabic language. Among several computational approaches that are used for automating phonological rules, this paper uses Artificial Neural Network (ANN) approach, and thus, contributes the using of ANN as a computational approach for automating the assimilation rules in the Arabic language. The designed ANN-based system of this paper has been defined and implemented by using MATLAB software, in which the results show the success of this approach and deliver an experience for later similar work.

Speaker recognition based on Arabic phonemes

In this paper, we investigate the effect of Arabic phonemes on the performance of speaker recognition systems. The investigation reveals that some Arabic phonemes have a strong effect on the recognition rate of such systems. The performance of speaker recognition systems can be improved and their execution time can be reduced by utilizing this finding. Additionally, this finding can be used by segmenting the most effective phonemes for speaker recognition from the utterance, using only the segmented part of the speech for speaker recognition. It can also be used in designing the text to be used in high-performance speaker recognition systems. From our investigation, we find that the recognition rates of Arabic vowels were all above 80%, whereas the recognition rates for the consonants varied from very low (14%) to very high (94%), with the latter achieved by a pharyngeal consonant followed by the two nasal phonemes, which achieved recognition rates above 80%. Four more consonants had recognition rates between 70% and 80%. We show that by utilizing these findings and by designing the text carefully, we can build a high-performance speaker recognition system.

Building CMU Sphinx language model for the Holy Quran using simplified Arabic phonemes

This paper investigates the use of a simplified set of Arabic phonemes in an Arabic Speech Recognition system applied to Holy Quran. The CMU Sphinx 4 was used to train and evaluate a language model for the Hafs narration of the Holy Quran. The building of the language model was done using a simplified list of Arabic phonemes instead of the mainly used Romanized set in order to simplify the process of generating the language model. The experiments resulted in very low Word Error Rate (WER) reaching 1.5% while using a very small set of audio files during the training phase when using all the audio data for both the training and the testing phases. However, when using 90% and 80% of the training data, the WER obtained was respectively 50.0% and 55.7%.

Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition

In attempt to increase the rate of Arabic phonemes recognition, we introduce a novel hybrid recognition algorithm. The algorithm is composed of the learning vector quantization (LVQ) and hidden Markov model (HMM). The hybrid algorithm used to recognizing Arabic phonemes in continuous open-vocabulary speech. A recorded Arabic corpus of different TV news for modern standard Arabic was used for training and testing purposes. We employ a data driven approach to generate the training feature vectors that embed the frame neighboring correlation information. Next, we generate the phonemes codebooks using the K-means splitting algorithm. Then, we trained the generated codebooks using the LVQ algorithm. We achieved a performance of 98.49 % during independent classification training and 90 % during dependent classification training. When using the trained LVQ codebooks in Arabic utterance transcription, the phoneme recognition rate was 72 % using LVQ only. We combined the LVQ codebooks with the single state HMM model using enhanced Viterbi algorithm which includes the phonemes bigrams. We achieved 89 % of Arabic phonemes recognition rate based on the hybrid LVQ/HMM algorithm.

Spelling Proficiency of Novel Phonemes in Arabic among Native Hebrew Academic Students

This study examined spelling proficiency in Arabic among native Hebrew speaking students (n=50) learning the written form of Arabic as a foreign language (AFL) in the Middle Eastern Studies department at one of the largest Universities in south Israel. Specifically, this study focused on ten novel phonemes (guttural, velar-uvular, dental, and emphatic). Three tasks were performed through the computer: a visual task, an auditory task and an audiovisual task. The results support previous studies which found that native Hebrew speakers learning AFL cope with extra linguistic burden, due to the fact that several Arabic phonemes visually resemble other Arabic graphemes while simultaneously corresponding with an Arabic phoneme that is similar to a familiar phoneme from the Hebrew phonemic inventory. The results also indicated that the students had difficulties representing novel phonemes which do not exist in their mother tongue, especially emphatic and dental phonemes. In addition, lower scores were achieved in the auditory task vs. the visual task and the audio-visual task. The students performed significantly better on the auditory task after two years of AFL learning, yet the scores still remained lower than the scores for the other tasks. This may be explained by the AFL curriculum and instruction, which mainly focuses on the development of reading and writing skills, while at the same time neglecting aural-oral skills in Arabic teaching. Thus, it is highly recommended to use aural-oral skills alongside the practice of other aspects of the language in order to establish successful and meaningful learning of Arabic. Arabic is the second official language of Israel, after Hebrew. It is being learned as a second foreign language, after English, in the Educational system in Hebrew schools from seventh to tenth grades. Arabic language, literature and culture are also studied in most Israeli Universities and in some colleges. Recent studies of novel phoneme representations among native Hebrew speaking pupils learning Arabic as a foreign language (AFL) in junior high school in Israel, indicated that native Hebrew speaking pupils had very low proficiency in grapheme-phoneme representations of novel phonemes even after four years of exposure and practice (Fragman & Russak, 2010; Russak & Fragman,

Cascade–forward Neural Networks for Arabic Phonemes Based on k–Fold Cross Validation

The study of Malaysian Arabic phoneme is rarely found which make the references to the work is difficult. Specific guideline on Malaysian subject is not found even though a lot of acoustic and phonetics research has been done on other languages such as English, French and Chinese. In this paper, we monitored and analyzed the performance of cascade-forward (CF) networks on our phoneme recognition system of Standard Arabic (SA). This study focused on Malaysian children as test subjects. It is focused on four chosen phonemes from SA, which composed of nasal, lateral and trill behaviors, i.e. tabulated at four different articulation places. Cascade neural networks are chosen as it provide less time for samples processing. The method, k–fold cross validation to evaluate each network architecture in k times to improve the reliability of the choice of the optimal architecture. Based on this method, namely 10–fold cross validation, the most suitable cascade–layer network architecture in first hidden layer and second hidden layer is 40 and 10 nodes respectively with MSE 0.0402. The training and testing recognition rates achieved were 94% and 93% respectively.

Primary Research on Arabic Visemes, Analysis in Space and Frequency Domain

Visemes are the unique facial positions required to produce phonemes, which are the smallest phonetic unit distinguished by the speakers of a particular language. Each language has multiple phonemes and visemes, and each viseme can have multiple phonemes. However, current literature on viseme research indicates that the mapping between phonemes and visemes is many-to-one: there are many phonemes which look alike visually, and hence they fall into the same visemic category. To evaluate the performance of the proposed method, the authors collected a large number of speech visual signal of five Algerian speakers male and female at different moments pronouncing 28 Arabic phonemes. For each frame the lip area is manually located with a rectangle of size proportional to 120*160 and centred on the mouth, and converted to gray scale. Finally, the mean and the standard deviation of the values of the pixels of the lip area are computed by using 20 images for each phoneme sequence to classify the visemes. The pitch analysis is investigated to show its variation for each viseme.

Difficulties of Standard Arabic Phonemes Spoken by Non-Arab Primary School Children based on Formant Frequencies

Problem statement: The study of Malaysian Arabic phoneme is rarely found which make the references work difficult. Specific guideline on Malaysian subject is not found even though a lot of acoustic and phonetics research has been done on other languages such as English, French and Chinese. Approach: This study discussed about the correct and simplest way of Arabic phonemes pronunciation in Malay accent. The International Phonetic Alphabet of Arabic chart was considered as the reference of every recorded speech samples using Malaysian children for their sound localization (makhraj point) of every alphabet. The recorded sound was analysed to determine the origin of each alphabet data by measuring its formant frequencies. The consonants of Standard Arabic (SA) phonemes were studied and the appropriate place of articulation of every phoneme was measured through its formant. Results: Only seven out of 25 consonants of SA phonemes of the children’s samples did not give the appropriate formants value. The formants are /kof/, [ ?], /zo/, [ ?], /kho/, [ ,[? /gheyn/, [ ?], /ha/, [ ?], /ain/, [ ?] & /ha/, [] which consider as the difficult SA to utter among Malaysian children. Conclusion/Recommendations: The values obtained are used as the reference of the database for our recognition system.

Speech Synthesis of Arabic Vocabularies

In this paper, system is presented for speech synthesis of Arabic vocabularies written with diacritics, graphemes transfer to allophones by applying the phonological rules. The system depend on phonetics units series every unite consists of one ore more phonetic character. We create linguistic group from Arabic phonemes and a group of phonological rules and special and unusual cases by designing special function for this purpose. We use (visual basic v.6) to design this system. Evaluation of the system was undertaken to assess the accuracy on word and sentence levels. The results showed high perception levels about 84 %.

Adaptation of foreign accented speakers in native Arabic ASR systems

This paper addresses the adaptation of Arabic speech recognition (ASR) systems to foreign accented speakers. This adaptation is accomplished by using the adaptation techniques; namely, the Maximum Likelihood Linear Regression (MLLR), the Maximum a posteriori (MAP), and the combination of MLLR and MAP. The LDC-WestPoint Modern Standard Arabic (MSA) corpus and HTK toolkit were used in implementing all experiments. The systems were evaluated using both word and phoneme levels. Results show that unique MSA Arabic Phonemes such as pharyngeal and emphatic consonants, which are difficult to pronounce for non-native speakers, benefit from the adaptation process using MLLR and MAP combination. An overall improvement of 7.37% has been obtained. This opens the eyes in benefiting from adaptation techniques in overcoming the difficulties of pronouncing nonnative language phonemes.

Arabic Speech Segmentation: Automatic Verses Manual Method and Zero Crossing Measurements

In this paper, a comparison between the automatic and manual approach of speech segmentation for the Arabic speech is conducted. In this approach, the automatic method, using the energy level measurement is compared to the manual segmentation procedure. The traditional zero crossing method commonly used for speech processing is also included in this work. The energy measurement method is based on dividing the uttered tokens into different levels. For instance, the Arabic language phonemes are divided into two energy regions: unvoiced phonemes which can be categorized as low energy include the sounds / س / (/s/) and / ه / (/h/). On the other hand, vowels and semi-vowels such as / / ( فتحه ) (/ ‘a /) and / و / (/w/) are labeled as high energy. Voiced fricatives, for instance, the sounds / ز / (/z/) and / /ع (/aa/) are classified as high energy phonemes.

The Linguistic Affiliation Constraint and phoneme recognition in diglossic Arabic

This study tested the effect of the phoneme's linguistic affiliation (Standard Arabic versus Spoken Arabic) on phoneme recognition among five-year-old Arabic native speaking kindergarteners (N=60). Using a picture selection task of words beginning with the same phoneme, and through careful manipulation of the phonological properties of target phonemes and distractors, the study showed that children's recognition of Standard phonemes was poorer than that of Spoken phonemes. This finding was interpreted as indicating a deficiency in the phonological representations of Standard words. Next, the study tested two hypotheses regarding the specific consequences of under-specified phonological representations: phonological encoding versus phonological processing. These hypotheses were addressed through an analysis of the relative power of distractors. The findings revealed that children's difficulty in accessing Standard Arabic phonemes was due to a difficulty in the phonological encoding of Standard words. We discuss the implications of the findings for language and literacy development in diglossic Arabic.

Arabic loanwords in Indonesian revisited

How did Arabic loanwords end up in Indonesian? Various academic answers to this intriguing question have been put forward, but only rarely has any solid linguistic 'proof' been provided. General conclusions have been drawn on the basis of a few, rather unrepresentative or obsolete, examples. Academics have copied arguments and hypotheses from one another, without any independent research into their reliability. Some of the arguments, or pure hypotheses, have obtained the status of pseudo 'established fact', irrespective of the fact that they lack academic justification. This article makes an effort to put the record straight by showing that a Persian or Persianized connection cannot be proven statistically, as claimed by Stuart Campbell. It also shows that some loanwords have a colloquial, occasionaly even regionally identifiable Arabic origin, but that the claim by Versteegh and others of a South Arabian or Hadrami connection cannot be convincingly established. It finally demonstrates that more attention should be given to the linguistic particularities of the recipient (Indonesian/Nusantara) language, than to concentrate mainly on the characteristics of the Arabic language which provided these loanwords. Unknown Arabic phonemes deserve special attention because they were in some cases interpreted and adopted by Indonesian/Nusantara listeners as phonemes that were phonetically nearest to these, and which were part of their own phoneme system.

GENETIC ALGORITHM APPLICATION TO THE STANDARD ARABIC PHONEMES CLASSIFICATION

The goal of this article is the application of genetic algorithms (GAs) to the automatic speech recognition (ASR) domain at the acoustic sequences classification level. Speech recognition has been cast as a pattern classification problem where we would like to classify an input acoustic signal into one of all possible phonemes. Also, the supervised classification has been formulated as a function optimization problem. Thus, we have attempted to recognize Standard Arabic (SA) phonemes of continuous, naturally spoken speech by using GAs, which have several advantages in resolving complicated optimization problems. In SA, there are 40 sounds. We have analyzed a corpus that contains several sentences composed of the whole SA phoneme types in the initial, medium, and final positions, recorded by several male speakers. Furthermore, the acoustic segments classification and the GAs have been explored. Among a set of classifiers such as Bayesian, likelihood, and distance classifier, we have used the distance classifier. It is based on the classification measure criterion. Therefore, we have used the decision rule Manhattan distance as the fitness functions for our GA evaluations. The corpus phonemes were extracted and classified successfully with an overall accuracy of 90.20%.

Experiments on Automatic Recognition of Nonnative Arabic Speech

The automatic recognition of foreign-accented Arabic speech is a challenging task since it involves a large number of nonnative accents. As well, the nonnative speech data available for training are generally insufficient. Moreover, as compared to other languages, the Arabic language has sparked a relatively small number of research efforts. In this paper, we are concerned with the problem of nonnative speech in a speaker independent, large-vocabulary speech recognition system for modern standard Arabic (MSA). We analyze some major differences at the phonetic level in order to determine which phonemes have a significant part in the recognition performance for both native and nonnative speakers. Special attention is given to specific Arabic phonemes. The performance of an HMM-based Arabic speech recognition system is analyzed with respect to speaker gender and its native origin. The West Point modern standard Arabic database from the language data consortium (LDC) and the hidden Markov Model Toolkit (HTK) are used throughout all experiments. Our study shows that the best performance in the overall phoneme recognition is obtained when nonnative speakers are involved in both training and testing phases. This is not the case when a language model and phonetic lattice networks are incorporated in the system. At the phonetic level, the results show that female nonnative speakers perform better than nonnative male speakers, and that emphatic phonemes yield a significant decrease in performance when they are uttered by both male and female nonnative speakers.