A Study Into Automatic Speaker Verification With Aspects Of Deep Learning
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A Study Into Automatic Speaker Verification with Aspects of Deep Learning
Advancements in automatic speaker verification (ASV) can be considered to be primarily limited to improvements in modelling and classification techniques, capable of capturing ever larger amounts of speech data. This thesis begins by presenting a fairly extensive review of developments in ASV, up to the current state-of-the-art with i-vectors and PLDA. A series of practical tuning experiments then follows. It is found somewhat surprisingly, that even the training of the total variability matrix required for i-vector extraction, is potentially susceptible to unwanted variabilities. The thesis then explores the use of deep learning in ASV. A literature review is first made, with two training methodologies appearing evident: indirectly using a deep neural network trained for automatic speech recognition, and directly with speaker related output classes. The review finds that interest in direct training appears to be increasing, underpinned with the intent to discover new robust 'speaker embedding' representations. Last a preliminary experiment is presented, investigating the use of a deep convolutional network for speaker identification. The small set of results show that the network successfully identifies two test speakers, out of 84 possible speakers enrolled. It is hoped that subsequent research might lead to new robust speaker representations or features.
Artificial Intelligence and Speech Technology
The 2nd International Conference on Artificial Intelligence and Speech Technology (AIST2020) was organized by Indira Gandhi Delhi Technical University for Women, Delhi, India on November 19–20, 2020. AIST2020 is dedicated to cutting-edge research that addresses the scientific needs of academic researchers and industrial professionals to explore new horizons of knowledge related to Artificial Intelligence and Speech Technologies. AIST2020 includes high-quality paper presentation sessions revealing the latest research findings, and engaging participant discussions. The main focus is on novel contributions which would open new opportunities for providing better and low-cost solutions for the betterment of society. These include the use of new AI-based approaches like Deep Learning, CNN, RNN, GAN, and others in various Speech related issues like speech synthesis, speech recognition, etc.
Proceedings of 5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications
Author: Vinit Kumar Gunjan
language: en
Publisher: Springer Nature
Release Date: 2025-02-25
This book contains original, peer-reviewed research articles from the 5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, held in Hyderabad, India on 28–29 March 2024. It includes the most recent research trends and advancements in machine learning, smart cities, IoT, AI, cyber-physical systems, cybernetics, data science, neural networks, and cognition. This book addresses the comprehensive nature of AI, ML, and DL to highlight its role in the modelling, identification, optimisation, prediction, forecasting, and control of future intelligent systems.