Mohamed I. Alkanhal

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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.(More)
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(More)
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(More)
This paper describes the process of collecting and recording a large scale Arabic single speaker speech corpus. The collection and recording of the corpus was supervised by professional linguists and was recorded by a professional speaker in a soundproof studio using specialized equipments and stored in high quality formats. The pitch of the speaker (EGG)(More)
Correlation filters have shown good performance results for distortion tolerant applications especially in target and face recognition problems. In this paper, we investigate the performance of these filters when applied to partially occluded human faces. We present a system for eye region recognition based on a special class of unconstrained correlation(More)
This paper describes a nonlinear face recognition method based on polynomial spatial frequency image processing. This nonlinear method is known as the polynomial distance classifier correlation filter (PDCCF). PDCCF is a member of a well-known family of filters called correlation filters. Correlation filters are attractive because of their shift invariance(More)
Predicting human behavior has been the subject of many research areas especially in machine learning. Due to its potential benefits, financially or otherwise, researchers have focused on modeling human behavior from recommending items in an online store to predicting the behavior of an entire ecosystem. In this paper, we make an attempt to predict human(More)