Speech recognition system of Arabic alphabet based on a telephony Arabic corpus

  • Authors:

  • Yousef Ajami Alotaibi;Mansour Alghamdi;Fahad Alotaiby

  • Affiliations:

  • Computer Engineering Department, King Saud University, Riyadh, Saudi Arabia;King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia;Department of Electrical Engineering, King Saud University, Riyadh, Saudi Arabia

  • Venue:
  • ICISP'10 Proceedings of the 4th international conference on Image and signal processing
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Automatic recognition of spoken alphabets is one of the difficult tasks in the field of computer speech recognition. In this research, spoken Arabic alphabets are investigated from the speech recognition problem point of view. The system is designed to recognize spelling of an isolated word. The Hidden Markov Model Toolkit (HTK) is used to implement the isolated word recognizer with phoneme based HMM models. In the training and testing phase of this system, isolated alphabets data sets are taken from the telephony Arabic speech corpus, SAAVB. This standard corpus was developed by KACST and it is classified as a noisy speech database. A hidden Markov model based speech recognition system was designed and tested with automatic Arabic alphabets recognition. Four different experiments were conducted on these subsets, the first three trained and tested by using each individual subset, the fourth one conducted on these three subsets collectively. The recognition system achieved 64.06% overall correct alphabets recognition using mixed training and testing subsets collectively.