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Dive into the research topics where Mansour M. Alghamdi is active.

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Featured researches published by Mansour M. Alghamdi.


Information Sciences | 2002

Techniques for high quality Arabic speech synthesis

Moustafa Elshafei; Husni Al-Muhtaseb; Mansour M. Alghamdi

The paper proposes a diphone/sub-syllable method for Arabic Text-to-Speech (ATTS) systems. The proposed approach exploits the particular syllabic structure of the Arabic words. For good quality, the boundaries of the speech segments are chosen to occur only at the sustained portion of vowels. The speech segments consists of consonants-half vowels, half vowel-consonants, half vowels, middle portion of Vowels, and suffix consonants. The minimum set consists of about 310 segments for classical Arabic.


IEEE Transactions on Audio, Speech, and Language Processing | 2012

Automatic Stochastic Arabic Spelling Correction With Emphasis on Space Insertions and Deletions

Mohamed I. Alkanhal; Mohamed Al-Badrashiny; Mansour M. Alghamdi; Abdulaziz O. Al-Qabbany

This paper presents a stochastic-based approach for misspelling correction of Arabic text. In this approach, a context-based two-layer system is utilized to automatically correct misspelled words in large datasets. The first layer produces a list in which possible alternatives for each misspelled word are ranked using the Damerau-Levenshtein edit distance. The same layer also considers merged and split words resulting from deletion and insertion of space character. The right alternative for each misspelled word is stochastically selected based on the maximum marginal probability via A* lattice search and m-gram probability estimation. A large dataset was utilized to build and test the system. The testing results show that as we increase the size of the training set, the performance improves reaching 97.9% of F1 score for detection and 92.3% of F1 score for correction.


Journal of King Saud University - Computer and Information Sciences archive | 2008

Saudi Accented Arabic Voice Bank

Mansour M. Alghamdi; Fayez Alhargan; Mohammed I. Alkanhal; Ashraf Alkhairy; Munir M. El-Desouki; Ammar Alenazi

The aim of this paper is to present an Arabic speech database that represents Arabic native speakers from all the cities of Saudi Arabia. The database is called the Saudi Accented Arabic Voice Bank (SAAVB). Preparing the prompt sheets, selecting the right speakers and transcribing their speech are some of the challenges that faced the project team. The procedures that meet these challenges are highlighted. SAAVB consists of 1033 speakers speak in Modern Standard Arabic with a Saudi accent. The SAAVB content is analyzed and the results are illustrated. The content was verified internally and externally by IBM Cairo and can be used to train speech engines such as automatic speech recognition and speaker verification systems.


International Journal of Speech Technology | 2007

Arabic broadcast news transcription system

Mansour M. Alghamdi; Moustafa Elshafei; Husni Al-Muhtaseb

This paper describes the development of an Arabic broadcast news transcription system. The presented system is a speaker-independent large vocabulary natural Arabic speech recognition system, and it is intended to be a test bed for further research into the open ended problem of achieving natural language man-machine conversation. The system addresses a number of challenging issues pertaining to the Arabic language, e.g. generation of fully vocalized transcription, and rule-based spelling dictionary. The developed Arabic speech recognition system is based on the Carnegie Mellon university Sphinx tools. The Cambridge HTK tools were also utilized at various testing stages.The system was trained on 7.0 hours of a 7.5 hours of Arabic broadcast news corpus and tested on the remaining half an hour. The corpus was made to focus on economics and sport news. At this experimental stage, the Arabic news transcription system uses five-state HMM for triphone acoustic models, with 8 and 16 Gaussian mixture distributions. The state distributions were tied to about 1680 senons. The language model uses both bi-grams and tri-grams. The test set consisted of 400 utterances containing 3585 words. The Word Error Rate (WER) came initially to 10.14 percent. After extensive testing and tuning of the recognition parameters the WER was reduced to about 8.61% for non-vocalized text transcription.


Journal of Information Technology Research | 2009

Arabic Phonetic Dictionaries for Speech Recognition

Mohamed Ali; Moustafa Elshafei; Mansour M. Alghamdi; Husni Al-Muhtaseb

Phonetic dictionaries are essential components of large-vocabulary speaker-independent speech recognition systems. This paper presents a rule-based technique to generate phonetic dictionaries for a large vocabulary Arabic speech recognition system. The system used conventional Arabic pronunciation rules, common pronunciation rules of Modern Standard Arabic, as well as some common dialectal cases. The paper gives in detail an explanation of these rules as well as their formal mathematical presentation. The rules were used to generate a dictionary for a 5.4 hour corpus of broadcast news. The rules and the phone set were tested and evaluated on an Arabic speech recognition system. The system was trained on 4.3 hours of the 5.4 hours of Arabic broadcast news corpus and tested on the remaining 1.1 hours. The phonetic dictionary contains 23,841 definitions corresponding to about 14232 words. The language model contains both bi-grams and tri-grams. The Word Error Rate (WER) came to 9.0%.


international conference on innovations in information technology | 2008

Generation of arabic phonetic dictionaries for speech recognition

Mohamed Ali; Moustafa Elshafei; Mansour M. Alghamdi; Husni Al-Muhtaseb; Atef J. Al-Najjar

Phonetic dictionaries are essential components of large-vocabulary natural language speaker-independent speech recognition systems. This paper presents a rule-based technique to generate Arabic phonetic dictionaries for a large vocabulary speech recognition system. The system used classic Arabic pronunciation rules, common pronunciation rules of Modern Standard Arabic, as well as morphologically driven rules. The paper gives in detail an explanation of these rules as well as their formal mathematical presentation. The rules were used to generate a dictionary for a 5.4 hours corpus of broadcast news. The phonetic dictionary contains 23,841 definitions corresponding to about 14232 words. The generated dictionary was evaluated on an actual Arabic speech recognition system. The pronunciation rules and the phone set were validated by test cases. The Arabic speech recognition system achieves word error rate of %11.71 for fully diacritized transcription of about 1.1 hours of Arabic broadcast news.


information sciences, signal processing and their applications | 2007

Speaker verification based on Saudi accented Arabic database

Mohamed I. Alkanhal; Mansour M. Alghamdi; Zeeshan Muzaffar

Speaker verification is concerned with verifying the speakerpsilas claimed identity. This paper reports on recent experiments we carried out for speaker verification using a Saudi accented Arabic telephone speech database with 1033 speakers. Gaussian Mixture Model was employed in these experiments. In speaker verification, users might produce two or more utterances. We show that we can reduce error rates by combining scores of these utterances.


Ingénierie Des Systèmes D'information | 2014

Phoneme-Based Recognizer to Assist Reading the Holy Quran

Yahya O. Mohamed Elhadj; Mansour M. Alghamdi; Mohammad Ibrahim Alkanhal

This paper presents a new phase of our ongoing efforts for building a high performance speaker independent recognizer for Quran recitation. An in-house developed and annotated sound database of about eight hours is used for this purpose. Since this sound database is segmented and annotated on both allophone and phoneme levels, we are developing two separate baseline recognizers for respectively allophones and phonemes. We employed the same approach for developing both phoneme and allophone recognizers to be able to make some kind of comparison between them. The Cambridge HTK tools are used for the development of these recognizers. We present in this paper the development of the phoneme-based recognizer to measure its appropriateness for the sake of our ultimate goal of building a high performance speaker independent recognizer to assist reading and memorizing the Holy Quran; the details of the allophonic recognizer is being published separately. Each Quarnic phoneme is modeled by an acoustic Hidden Markov Model (HMM) with 3-emitting states. A continues probability distribution using 16 Gaussian mixture distributions is used for each emitting state. Results give 92% of average recognition rate, which is very promising, compared to 88% for the allophonic recognizer.


Journal of King Saud University - Computer and Information Sciences archive | 2004

Algorithms for Romanizing Arabic Names

Mansour M. Alghamdi

People such as immigration personnel who work on the Romanization of Arabic names find it troublesome and sometimes confusing. One reason for such difficulty is that an Arabic name can be transliterated in different forms because of the absence of common standards. Another reason is that even if the transliteration of Arabic names is standardized, it is difficult for a layperson to implement it. The first obstacle has been overcome after the Symposium on Standardizing Arabic Name Transliteration Security Dimensions which one of its outcomes was standardized measures for Arabic name transliteration. This paper is to provide algorithms based on the symposium standards that can be used in programming a system to transliterate Arabic names automatically.


international conference on image and signal processing | 2010

Speech recognition system of Arabic alphabet based on a telephony Arabic corpus

Yousef Ajami Alotaibi; Mansour M. Alghamdi; Fahad Alotaiby

Automatic recognition of spoken alphabets is one of the difficult tasks in the field of computer speech recognition. In this research, spoken Arabic alphabets are investigated from the speech recognition problem point of view. The system is designed to recognize spelling of an isolated word. The Hidden Markov Model Toolkit (HTK) is used to implement the isolated word recognizer with phoneme based HMM models. In the training and testing phase of this system, isolated alphabets data sets are taken from the telephony Arabic speech corpus, SAAVB. This standard corpus was developed by KACST and it is classified as a noisy speech database. A hidden Markov model based speech recognition system was designed and tested with automatic Arabic alphabets recognition. Four different experiments were conducted on these subsets, the first three trained and tested by using each individual subset, the fourth one conducted on these three subsets collectively. The recognition system achieved 64.06% overall correct alphabets recognition using mixed training and testing subsets collectively.

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Husni Al-Muhtaseb

King Fahd University of Petroleum and Minerals

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Moustafa Elshafei

King Fahd University of Petroleum and Minerals

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Mohamed I. Alkanhal

King Abdulaziz City for Science and Technology

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Abdulaziz O. Al-Qabbany

King Abdulaziz City for Science and Technology

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Wasfi G. Al-Khatib

King Fahd University of Petroleum and Minerals

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Ashraf Alkhairy

King Abdulaziz City for Science and Technology

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Khalid M.O Nahar

King Fahd University of Petroleum and Minerals

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Mohammed I. Alkanhal

King Abdulaziz City for Science and Technology

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