Arabic is among the most challenging languages in the world. Unfortunately, the scarcity of Arabic datasets makes studies in Arabic speech technology demanding. This paper introduces SADA, the Saudi Audio Dataset for Arabic, with 668 hours of high-quality audio suitable for supervised training. The audio recordings were sourced from 57 television shows provided by the Saudi Broadcasting Authority. The audio covers both read and spontaneous speaking styles in various genres. The National Center for Artificial Intelligence in Saudi Arabia transcribed and prepared the data for training and processing. The recordings are in Arabic. Most are in Saudi dialects, while other Arabic dialects include Yemeni, Egyptian, and Levantine. The dataset is split into training, validation, and testing sets to enhance its usage. The validation and testing sets contain 10 hours of audio segments each. Besides giving a detailed description of the dataset, wide range of speech recognition experiments using standard tools are also presented.

Phrase reordering is a challenge for statis-tical machine translation systems. Posing phrase movements as a prediction prob-lem using contextual features modeled by maximum entropy-based classifier is su-perior to the commonly used lexicalized reordering model. However, Training this discriminative model using large-scale parallel corpus might be computationally expensive. In this paper, we explore recent advancements in solving large-scale clas-sification problems. Using the dual prob-lem to multinomial logistic regression, we managed to shrink the training data while iterating and produce significant saving in computation and memory while preserv-ing the accuracy. 1

Stochastic gradient descent has been advanced as a computationally efficient method for large-scale problems. In classification problems, many proposed linear support vector machines are very effective. However, they assume that the data is already in memory which might be not always the case. Recent work suggests a classical method that divides such a problem into smaller blocks then solves the sub-problems iteratively. We show that a simple modification of shrinking the dataset early will produce significant saving in computation and memory. We further find that on problems larger than previously considered, our approach is able to reach solutions on top-end desktop machines while competing methods cannot.