@InProceedings{messaoud2020_455,
<br/>   author = {Mohamed Anouar Ben messaoud and Aicha Bouzid},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020},
<br/>   title = {Speaker Gender Classification based on an Improved Deep Learning Approach},
<br/>   year = {2020},
<br/>   editor = {Andreas Wendemuth and Ronald Böck and Ingo Siegert},
<br/>   month = mar,
<br/>   pages = {193--198},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {With the great evolution of technology, Speaker gender and age classification
is one of the major problems for large range of applications in speech analysis and recognition.
The identification of speakers has become crucial in the cases of criminal suspect,
speech recognition, speech emotion, and computer-aided physiological. To improve the accuracy
of speaker gender classification, we must generate robust features with a depth classifier.
With the promising results giving by machine learning for classification problem, our approach
has taken advantage of deep learning. In this paper, we propose to apply a speaker
gender classification based on the Recurrent Neural Network (RNN) which is able to determine
the long term dependencies of a sequential speech signal. The most popular RNN is
Long Short-Term Memory (LSTM) model. However, it has a complex design which makes it
difficult to implement. So, we refine the LSTM model to our proposed Simplified Gated Recurrent
Units (SGRUs) by using an efficient architecture with only two multiplicative gates
more suitable for speech classification. Our approach is decomposed into two essential steps.
First, we generate the features from our model train based on SGRUs by removing of reset
gates to limit redundancy and reduce the number of parameters without affect the system performance.
Second, we use the Rectified Linear Units (ReLU) activations to learn long-term
dependencies without slow down the training process. In Our architecture, we modify the level
of dropout and increase the depth of the network. The architecture was tried on a public
challenging database. Experiment results show that our approach presenting a high accuracy
surpassing other recent methods of gender classification task.},
<br/>   isbn = {978-3-959081-93-1},
<br/>   issn = {0940-6832},
<br/>   keywords = {Poster},
<br/>   url = {https://www.essv.de/pdf/2020_193_198.pdf},
<br/>}