Title :
Improved tree model for arabic speech recognition
Author :
Hammami, Nacereddine ; Bedda, Mouldi
Author_Institution :
Fac. of Comput. & Inf. Sci., Univ. of Al Jouf, Sakaka, Saudi Arabia
Abstract :
This paper introduces a fast learning method for a graphical probabilistic model for discrete speech recognition based on spoken Arabic digit recognition by means of a new proposed spanning tree structure that takes advantage of the temporal nature of speech signal. The experimental results obtained on a spoken Arabic digit dataset confirmed that for the same rate of recognition the proposed method, in terms of time computation is much faster than the state of art algorithm that use the maximum weight spanning tree (MWST).
Keywords :
learning (artificial intelligence); natural language processing; probability; speech recognition; tree data structures; Arabic digit recognition; Arabic speech recognition; discrete speech recognition; fast learning method; graphical probabilistic model; improved tree model; spanning tree structure; Hidden Markov models; Arabic Speech recognition; dependency tree; discrete probability distributions; graphical model; optimal-spanning-tree;
Conference_Titel :
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5537-9
DOI :
10.1109/ICCSIT.2010.5563892
