Deep Learning For Speech Classification And Speaker Recognition
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Deep Learning for Speech Classification and Speaker Recognition
Deep learning is the state-of-the-art technique in machine learning with applications in speech recognition. In this study, an efficient system is formulated to process large amounts of speech data within the deep learning framework by harnessing the parallel processing power of High-Performance Computing oriented Graphics Processing Unit (GPU). This thesis focuses on applications of this approach to address stressed speech classification as well as discrimination between different flavors of noise-free speech under Lombard Effect. Different architectures of deep neural networks (DNN) are explored to build state-of-the-art classifiers for detection and classification of stressed speech and Lombard Effect flavors. Furthermore, applications of deep networks are explored to improve current state-of-the-art speaker recognition systems. Further integration of discriminative deep architectures is accomplished for unsupervised methods in training front-ends for Speaker Recognition Evaluation systems.
Speaker Classification I
This volume and its companion volume LNAI 4441 constitute a state-of-the-art survey in the field of speaker classification. Together they address such intriguing issues as how speaker characteristics are manifested in voice and speaking behavior. The nineteen contributions in this volume are organized into topical sections covering fundamentals, characteristics, applications, methods, and evaluation.
Machine Learning for Speaker Recognition
Author: Man-Wai Mak
language: en
Publisher: Cambridge University Press
Release Date: 2020-11-19
This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.