Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Experiments on Automatic Recognition of Nonnative Arabic Speech | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2008 Article ID 679831 9 pages doi 2008 679831 Research Article Experiments on Automatic Recognition of Nonnative Arabic Speech Yousef Ajami Alotaibi 1 Sid-Ahmed Selouani 2 and Douglas O Shaughnessy3 1 Computer Engineering Department King Saud University Riyadh 11451 Saudi Arabia 2Laboratoire de Recherche en Interactivité Homme Système LARIHS Université de Moncton Campus de Shippagan New Brunswick Canada E8S 1P6 3INRS-Energie-Matériaux-Télécommunications Universitédu Québec 800 de la Gauchetière Ouest place Bonaventure Montréal Canada H5A 1K6 Correspondence should be addressed to Sid-Ahmed Selouani selouani@ Received 11 May 2007 Revised 5 October 2007 Accepted 13 January 2008 Recommended by Li Deng The automatic recognition of foreign-accented Arabic speech is a challenging task since it involves a large number of nonnative accents. As well the nonnative speech data available for training are generally insufficient. Moreover as compared to other languages the Arabic language has sparked a relatively small number of research efforts. In this paper we are concerned with the problem of nonnative speech in a speaker independent large-vocabulary speech recognition system for modern standard Arabic MSA . We analyze some major differences at the phonetic level in order to determine which phonemes have a significant part in the recognition performance for both native and nonnative speakers. Special attention is given to specific Arabic phonemes. The performance of an HMM-based Arabic speech recognition system is analyzed with respect to speaker gender and its native origin. The WestPoint modern standard Arabic database from the language data consortium LDC and the hidden Markov Model Toolkit HTK are used throughout all experiments. Ourstudy shows that the best performance in the overall phoneme recognition is obtained when nonnative speakers are involved in both .
