Summary of the paper

Title Vocal Pathologies Detection and Mispronounced Phonemes Identification: Case of Arabic Continuous Speech
Authors Naim Terbeh and Mounir Zrigui
Abstract We propose in this work a novel acoustic phonetic study for Arabic people suffering from language disabilities and non-native learners of Arabic language to classify Arabic continuous speech to pathological or healthy and to identify phonemes that pose pronunciation problems (case of pathological speeches). The main idea can be summarized in comparing between the phonetic model reference to Arabic spoken language and that proper to concerned speaker. For this task, we use techniques of automatic speech processing like forced alignment and artificial neural network (ANN) (Basheer, 2000). Based on a test corpus containing 100 speech sequences, recorded by different speakers (healthy/pathological speeches and native/foreign speakers), we attain 97% as classification rate. Algorithms used in identifying phonemes that pose pronunciation problems show high efficiency: we attain an identification rate of 100%.
Topics Voice Command and Control, Dialogue, Phonetic Databases, Phonology
Full paper Vocal Pathologies Detection and Mispronounced Phonemes Identification: Case of Arabic Continuous Speech
Bibtex @InProceedings{TERBEH16.640,
  author = {Naim Terbeh and Mounir Zrigui},
  title = {Vocal Pathologies Detection and Mispronounced Phonemes Identification: Case of Arabic Continuous Speech},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
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