Multi-agent Based Arabic Speech Recognition

This paper presents a novel agent-based design for Arabic speech recognition. We define the Arabic speech recognition as a multi-agent-system where each agent has a specific goal and deals with that goal only. Once all the small tasks are accomplished the big task is too. A number of agents are required in order to recast Arabic speech recognition, namely the feature extraction agent and the pattern classification agent. These agents are detailed in this paper.

[1]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[2]  Peter Norvig,et al.  Artificial intelligence - a modern approach: the intelligent agent book , 1995, Prentice Hall series in artificial intelligence.

[3]  M.A. Al-Alaoui,et al.  Arabic speech recognition using recurrent neural networks , 2003, Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No.03EX795).

[4]  Roberto Gemello,et al.  Ear-model derived features for automatic speech recognition , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[5]  Jacques Ferber,et al.  Multi-agent systems - an introduction to distributed artificial intelligence , 1999 .

[6]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[7]  Dimitra Vergyri,et al.  Automatic Diacritization of Arabic for Acoustic Modeling in Speech Recognition , 2004 .

[8]  Oded Ghitza,et al.  Temporal non-place information in the auditory-nerve firing patterns as a front-end for speech recognition in a noisy environment , 1988 .

[9]  Andreas Stolcke,et al.  Morphology-based language modeling for arabic speech recognition , 2004, INTERSPEECH.

[10]  Philip N. Garner,et al.  Using formant frequencies in speech recognition , 1997, EUROSPEECH.

[11]  Richard M. Golden,et al.  Mathematical Methods for Neural Network Analysis and Design , 1996 .

[12]  K. K. Paliwal,et al.  On the use of line spectral frequency parameters for speech recognition , 1992, Digit. Signal Process..

[13]  Andreas Stolcke,et al.  Morphology-based language modeling for conversational Arabic speech recognition , 2006, Computer Speech and Language.

[14]  Ya'akov Gal.  An HMM Approach to Vowel Restoration in Arabic and Hebrew , 2002, SEMITIC@ACL.

[15]  Climent Nadeu,et al.  On the decorrelation of filter-bank energies in speech recognition , 1995, EUROSPEECH.

[16]  H. Hermansky,et al.  An efficient speaker-independent automatic speech recognition by simulation of some properties of human auditory perception , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  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)..