  This paper proposes a novel approach to an automatic estimation of three
speaker traits from Arabic speech: gender, emotion, and dialect. After showing
promising results on different text classification tasks, the multi-task
learning (MTL) approach is used in this paper for Arabic speech classification
tasks. The dataset was assembled from six publicly available datasets. First,
The datasets were edited and thoroughly divided into train, development, and
test sets (open to the public), and a benchmark was set for each task and
dataset throughout the paper. Then, three different networks were explored:
Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), and
Fully-Connected Neural Network (FCNN) on five different types of features: two
raw features (MFCC and MEL) and three pre-trained vectors (i-vectors,
d-vectors, and x-vectors). LSTM and CNN networks were implemented using raw
features: MFCC and MEL, where FCNN was explored on the pre-trained vectors
while varying the hyper-parameters of these networks to obtain the best results
for each dataset and task. MTL was evaluated against the single task learning
(STL) approach for the three tasks and six datasets, in which the MTL and
pre-trained vectors almost constantly outperformed STL. All the data and
pre-trained models used in this paper are available and can be acquired by the
public.
