A Study of Voice Recognition System Using Deep Learning Techniques
-
R. Regan
Abstract
As there has been a tremendous development in “on-the-fly” computing technology, it becomes very essential to secure the system from unauthorized users and hackers. The biometrics verification system is one of the technological advancements in security measurements to restrict the unwanted users from accessing the systems. A behavioral biometric recognition and verification system performs the functions of reading unique biometric identity of an individual, such as fingerprint, iris, voice, and face, and authenticates the individual by comparing with the biometrics of the same kind in the database. As the above said behavioral biometric value can never be the same for two persons, the biometric verification and authentication system is highly accurate and reliable. The voice recognition and authentication system is one of the familiar and upcoming system that is taken for discussion in the chapter. To add more accuracy and intelligence flavor to the biometric especially voice-based verification and authentication, deep leaning is adopted as a pre-verification filter to separate out and remove bad or malicious individual from accessing the system. Deep learning puts core architectural building blocks together to build a new voice-based verification specific architecture. Deep learning allows the system to train itself again and again with test samples and makes the behavioral voice-based verification and authentication system (BVVAS) to be more secure and achieves high level of accuracy by reducing false identification. The deep learning supported BVVAS appears to be obviously fit for handling the everrising biometric identification problems ranging from mobile device to high-end security systems authentication.
Abstract
As there has been a tremendous development in “on-the-fly” computing technology, it becomes very essential to secure the system from unauthorized users and hackers. The biometrics verification system is one of the technological advancements in security measurements to restrict the unwanted users from accessing the systems. A behavioral biometric recognition and verification system performs the functions of reading unique biometric identity of an individual, such as fingerprint, iris, voice, and face, and authenticates the individual by comparing with the biometrics of the same kind in the database. As the above said behavioral biometric value can never be the same for two persons, the biometric verification and authentication system is highly accurate and reliable. The voice recognition and authentication system is one of the familiar and upcoming system that is taken for discussion in the chapter. To add more accuracy and intelligence flavor to the biometric especially voice-based verification and authentication, deep leaning is adopted as a pre-verification filter to separate out and remove bad or malicious individual from accessing the system. Deep learning puts core architectural building blocks together to build a new voice-based verification specific architecture. Deep learning allows the system to train itself again and again with test samples and makes the behavioral voice-based verification and authentication system (BVVAS) to be more secure and achieves high level of accuracy by reducing false identification. The deep learning supported BVVAS appears to be obviously fit for handling the everrising biometric identification problems ranging from mobile device to high-end security systems authentication.
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
