Speaker identification model based on deep nural netwoks
Saadaldeen Rashid Ahmed,Zainab Ali Abbood,hameed Mutlag Farhan,Baraa Taha Yasen,Mohammed Rashid Ahmed,Adil Deniz Duru +5 more
TLDR
This study aims to establish a small system of text-independent recognition of speakers for a relatively small group of speakers at a sound stage using the Direct Deep Neural Network (DNN)-based approach, in which the posterior opportunities of the output layer are utilized to determine the speaker’s presence.Abstract:
This study aims is to establish a small system of text-independent recognition of speakers for a
relatively small group of speakers at a sound stage. The fascinating justification for the International Space Station
(ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employed
Machine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, in
which the posterior opportunities of the output layer are utilized to determine the speaker’s presence. In line with
the small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hidden
units per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid the
normal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions,
validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multihandset communication database and TIMIT noise-added data framework, were tested for this reference model that
we developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruning
method in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned.
The usefulness of this approach was evaluated on all the above contact databasesread more
Citations
More filters
Journal ArticleDOI
The Practices of Artificial Intelligence Techniques and Their Worth in the Confrontation of COVID-19 Pandemic: A Literature Review
TL;DR: In this article , a set of important information about the vital role of artificial intelligence in the medical field is highlighted, and how this science does manage to confront SARS-CoV-2 by highlighting the investigations and analyses in predicting the spread of the virus, tracking infections, and diagnosis of cases through chest x-ray images of COVID-19 patients.
Journal ArticleDOI
Deepfake Audio Detection via MFCC Features Using Machine Learning
Ameer Hamza,Abdul Rehman Javed,Farkhund Iqbal,Natalia Kryvinska,Ahmad Almadhor,Zunera Jalil,Rouba Borghol +6 more
TL;DR: In this paper , the authors used machine and deep learning-based approaches to identify deepfake audio using the Mel-frequency cepstral coefficients (MFCCs) technique to acquire the most useful information from the audio.
Journal ArticleDOI
Deepfake Audio Detection via MFCC Features Using Machine Learning
TL;DR: In this article , the authors used machine and deep learning-based approaches to identify deepfake audio using the Mel-frequency cepstral coefficients (MFCCs) technique to acquire the most useful information from the audio.
Proceedings ArticleDOI
Vehicle Accident Avoidance System
TL;DR: In this paper , the authors presented the importance and role of technology in protecting human life regarding vehicle and road safety and presented a project to create a robotic vehicle that avoids accidents by detecting other nearby vehicles and then changing its path to a lane where it is safe or stopping in the worst case where there are cars in all nearby lanes.
Journal ArticleDOI
Target-aware Neural Architecture Search and Deployment for Keyword Spotting
TL;DR: In this paper , the authors present a design methodology based on Neural Architecture Search, exploited to combine the exploration of the optimal network topology, the audio pre-processing scheme, and the data quantization policy.