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This manuscript summarizes a novel study to detect and correct voice anomalies contained in Arabic discourses. These objectives are attained by following some fundamental steps. The first consists in classifying the Arabic produced healthy or pathological vocal signals. Second, the identification of problematic phonemes takes place. The proposition of algorithm allowing the correction of defective pronunciations presents the aim of the latter task. We are satisfied with the obtained results. Indeed, the elaborated algorithm has attained a correction performance of 90% based on 52 Arabic voice sequences covering male and female, healthy and pathological speeches and clustering several areas. Consequently, researchers of topics related to speech processing can benefit from our proposition in the conception and development of their systems.

The identification of Arabic dialects is considered to be the first pre-processing component for any natural language processing problem. This task is useful for automatic translation, information retrieval, opinion mining and sentiment analysis. In this purpose, we propose a statistical approach based on the phonetic modeling to identify the correspondent Arabic dialect for each input acoustic signal. The main idea consists first, and for each dialect, in calculating a referenced phonetic model. Second, for every input audio signal, we calculate an appropriate phonetic model. Third, we compare this latter to all referenced Arabic dialect models. Finally, we associate the input acoustic signal to the dialect where the referenced phonetic model minimizes the cosine similarity. The obtained results are satisfactory. Indeed, based on 117 audio sequences, we attain a classification rate of 93%. Supporting the achieved results and the coverage of most of Arabic dialects, this study can be a reference for future work addressing dialectical speech processing applications.

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