Study on pharyngeal and uvular consonants in foreign accented Arabic for ASR

This paper investigates the unique pharyngeal and uvular consonants of Arabic from the point of view of automatic speech recognition (ASR). Comparisons of the recognition error rates for these phonemes are analyzed in five experiments that involve different combinations of native and non-native Arabic speakers. The most three confusing consonants for every investigated consonant are discussed. All experiments use the Hidden Markov Model Toolkit (HTK) and the Language Data Consortium (LDC) WestPoint Modern Standard Arabic (MSA) database. Results confirm that these Arabic distinct consonants are a major source of difficulty for Arabic ASR. While the recognition rate for certain of these unique consonants such as /@?/ can drop below 35% when uttered by non-native speakers, there is advantage to include non-native speakers in ASR. Besides, regional differences in pronunciation of MSA by native Arabic speakers require the attention of Arabic ASR research.

[1]  Marwan Al-Zabibi.  An acoustic-phonetic approach in automatic arabic speech recognition , 1990 .

[2]  Shahid Masud,et al.  Arabic Phoneme Identification Using Conventional and Concurrent Neural Networks in Non Native Speakers , 2007, ICIC.

[3]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[4]  Yousif A. El-Imam.  An unrestricted vocabulary Arabic speech synthesis system , 1989, IEEE Trans. Acoust. Speech Signal Process..

[5]  Jeff A. Bilmes,et al.  Novel approaches to Arabic speech recognition: report from the 2002 Johns-Hopkins Summer Workshop , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[6]  John H. L. Hansen,et al.  Discrete-Time Processing of Speech Signals , 1993 .

[7]  Ulla Uebler,et al.  Multilingual speech recognition in seven languages , 2001, Speech Commun..

[8]  Genoveva Puskás,et al.  PHONETICS AND PHONOLOGY , 2002 .

[9]  Faisal Al-jasser,et al.  The Effect of Teaching English Phonotactics on the Lexical Segmentation of English as a Foreign Language. , 2008 .

[10]  J. Gandour,et al.  Phonetics and Phonology , 1998 .

[11]  Douglas D. O'Shaughnessy,et al.  Experiments on Automatic Recognition of Nonnative Arabic Speech , 2008, EURASIP J. Audio Speech Music. Process..

[12]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[13]  Laura Mayfield Tomokiyo,et al.  Linguistic properties of non-native speech , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[14]  Slim Ouni,et al.  Training Baldi to be multilingual: A case study for an Arabic Badr , 2005, Speech Commun..

[15]  Sid-Ahmed Selouani,et al.  Arabic phonetic features recognition using modular connectionist architectures , 1998, Proceedings 1998 IEEE 4th Workshop Interactive Voice Technology for Telecommunications Applications. IVTTA '98 (Cat. No.98TH8376).

[16]  Roger K. Moore.  Computer Speech and Language , 1986 .

[17]  Muhammad Ghulam,et al.  Pitch-synchronous ZCPA (PS-ZCPA)-based feature extraction with auditory masking , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[18]  Fatima Abdurahman Nureddeen.  Cross cultural pragmatics: Apology strategies in Sudanese Arabic , 2008 .

[19]  Biing-Hwang Juang,et al.  Hidden Markov Models for Speech Recognition , 1991 .

[20]  P. Delattre.  Pharyngeal Features in the Consonants of Arabic, German, Spanish, French, and American English , 1971 .