Abstract:

The literature seems rich with studies addressing the detection of pronunciation disorders. The features contained in the speech signal and natural language processing techniques present famous parameters used for this objective. Despite the diversity of factors posing pronunciation disorders (vocal pathologies, non-native speakers, psychological state, age, etc.), no work has been extended to correct pathological speeches. The current work presents an original approach based on the probabilistic-phonetic modeling of Arabic speech to detect vocal disorders (Terbeh, 2015). If the analyzed speech presents some degradations, an original algorithm will be proposed to correct pathological pronunciations. The present work accounts four steps. The first step consists in calculating the referenced phonetic model of the Arabic speech. This model will be used in detecting the vocal defects contained in the Arabic speech. Second, the referenced forced alignment scores for Arabic phonemes are calculated. Also, for each new speaker with vocal disorders, their forced alignment scores of non-problematic phonemes (Terbeh, 2016) are calculated. The two previous scores are compared to distinguish between the pronunciation disorders caused by native speakers suffering from vocal pathologies and by non-native speakers who do not master Arabic-phoneme pronunciation. The last task consists in developing an algorithm to correct the pathological pronunciation. We are satisfied with the obtained results. We have attained an identification rate of factors posing pronunciation disorders of 95%, a correction performance of 99%. Speech therapists, biologists and computer scientists can benefit from this work to develop performant systems of pathological speech processing: pathological speech recognition, accent evaluation, e-learning, etc.