Name: The Arabic Speech Corpus for Isolated Words
Description: The Arabic speech corpus for isolated words contains 9992 utterances of 20 words spoken by 50 native male Arabic speakers. It has been recorded with a 44100 Hz sampling rate and 16-bit resolution. This corpus is free for noncommercial uses in the raw format (.wav files) and other formats e.g. (MFCCs) are available under request.
Provider: University of Stirling
Paper Title: On Improving the Classification Capability of Reservoir Computing for Arabic Speech Recognition
Tasks: speech recognition 
Venue Name: nan
Authors: Abdulrahman Alalshekmubarak, Leslie S. Smith
Abstract: Designing noise-resilient systems is a major challenge in the
  field of automated speech recognition (ASR). These systems are crucial
  for real-world applications where high levels of noise tend to be present.
  We introduce a noise robust system based on Echo State Networks and
  Extreme Kernel machines which we call ESNEKM. To evaluate the performance of the proposed system, we used our recently released public
  Arabic speech dataset and the well-known spoken Arabic digits (SAD)
  dataset. Different feature extraction methods considered in this study
  include mel-frequency cepstral coefficients (MFCCs), perceptual linear
  prediction (PLP) and RASTA- perceptual linear prediction. These extracted features were fed to the ESNEKM and the result compared with
  a baseline hidden Markov model (HMM), so that nine models were compared in total. ESNEKM models outperformed HMM models under all
  the feature extraction methods, noise levels, and noise types. The best
  performance was obtained by the model that combined RASTA-PLP
  with ESNEKM.