The Arabic Speech Corpus for Isolated Words
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.
University of Stirling
On Improving the Classification Capability of Reservoir Computing for Arabic Speech Recognition
speech recognition 
nan
Abdulrahman Alalshekmubarak, Leslie S. Smith
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.