Self Learning Speaker Identification For Enhanced Speech Recognition
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Self Learning Speaker Identification
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Author : Tobias Herbig
language : en
Publisher: Springer
Release Date : 2011-07-28
Self Learning Speaker Identification written by Tobias Herbig and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-28 with Technology & Engineering categories.
Current speech recognition systems are based on speaker independent speech models and suffer from inter-speaker variations in speech signal characteristics. This work develops an integrated approach for speech and speaker recognition in order to gain space for self-learning opportunities of the system. This work introduces a reliable speaker identification which enables the speech recognizer to create robust speaker dependent models In addition, this book gives a new approach to solve the reverse problem, how to improve speech recognition if speakers can be recognized. The speaker identification enables the speaker adaptation to adapt to different speakers which results in an optimal long-term adaptation.
Self Learning Speaker Identification For Enhanced Speech Recognition
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Author : Tobias Herbig
language : en
Publisher:
Release Date : 2011
Self Learning Speaker Identification For Enhanced Speech Recognition written by Tobias Herbig and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Fundamentals Of Speaker Recognition
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Author : Homayoon Beigi
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-12-09
Fundamentals Of Speaker Recognition written by Homayoon Beigi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-12-09 with Technology & Engineering categories.
An emerging technology, Speaker Recognition is becoming well-known for providing voice authentication over the telephone for helpdesks, call centres and other enterprise businesses for business process automation. "Fundamentals of Speaker Recognition" introduces Speaker Identification, Speaker Verification, Speaker (Audio Event) Classification, Speaker Detection, Speaker Tracking and more. The technical problems are rigorously defined, and a complete picture is made of the relevance of the discussed algorithms and their usage in building a comprehensive Speaker Recognition System. Designed as a textbook with examples and exercises at the end of each chapter, "Fundamentals of Speaker Recognition" is suitable for advanced-level students in computer science and engineering, concentrating on biometrics, speech recognition, pattern recognition, signal processing and, specifically, speaker recognition. It is also a valuable reference for developers of commercial technology and for speech scientists. Please click on the link under "Additional Information" to view supplemental information including the Table of Contents and Index.
Self Learning Speaker Identification
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Author : Tobias Herbig
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-18
Self Learning Speaker Identification written by Tobias Herbig and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-18 with Technology & Engineering categories.
Current speech recognition systems are based on speaker independent speech models and suffer from inter-speaker variations in speech signal characteristics. This work develops an integrated approach for speech and speaker recognition in order to gain space for self-learning opportunities of the system. This work introduces a reliable speaker identification which enables the speech recognizer to create robust speaker dependent models In addition, this book gives a new approach to solve the reverse problem, how to improve speech recognition if speakers can be recognized. The speaker identification enables the speaker adaptation to adapt to different speakers which results in an optimal long-term adaptation.
Robustness Related Issues In Speaker Recognition
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Author : Thomas Fang Zheng
language : en
Publisher: Springer
Release Date : 2017-04-06
Robustness Related Issues In Speaker Recognition written by Thomas Fang Zheng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-06 with Technology & Engineering categories.
This book presents an overview of speaker recognition technologies with an emphasis on dealing with robustness issues. Firstly, the book gives an overview of speaker recognition, such as the basic system framework, categories under different criteria, performance evaluation and its development history. Secondly, with regard to robustness issues, the book presents three categories, including environment-related issues, speaker-related issues and application-oriented issues. For each category, the book describes the current hot topics, existing technologies, and potential research focuses in the future. The book is a useful reference book and self-learning guide for early researchers working in the field of robust speech recognition.
Speech And Speaker Recognition
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Author : Manfred Robert Schroeder
language : en
Publisher: Karger Medical and Scientific Publishers
Release Date : 1985-01-01
Speech And Speaker Recognition written by Manfred Robert Schroeder and has been published by Karger Medical and Scientific Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-01-01 with Medical categories.
Speaker Classification I
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Author : Christian Müller
language : en
Publisher: Springer
Release Date : 2007-08-28
Speaker Classification I written by Christian Müller and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-28 with Computers categories.
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.
Advancements In Domain Adaptation For Speaker Recognition And Effective Speaker De Identification
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Author : Fahimeh Bahmaninezhad
language : en
Publisher:
Release Date : 2020
Advancements In Domain Adaptation For Speaker Recognition And Effective Speaker De Identification written by Fahimeh Bahmaninezhad and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Automatic speech recognition categories.
Recent advancements in machine learning and artificial intelligence have significantly impacted the way humans interact with machines. Voice assistant based solutions are examples of emerging technology advancements that impact human-machine interaction. Since, speech is the most natural form of human communication, voice assistant devices have received wide user acceptance, and have become a pleasant way to facilitate and address everyday living needs, including access to the current news, events, etc. These voice-based technologies have been made possible through advanced robust processing of speech signals. Depending on the application, various speech processing techniques are required to achieve an effective overall robust solution. Speech recognition is required when text content of spoken words is needed; for example adding text captions to broadcast news or YouTube videos. If a service should become available based on who is interacting with the device, speaker recognition becomes a required step; for example, if an individual gains access to a data account (e.g., music, voice-mail, health or financial records), effective speaker recognition is needed for that service. Overall, a range of solutions in speech processing can be required to address an overall request. Other areas of speech processing that benefit the human-machine interaction include language/dialect recognition, speech enhancement, machine translation, speech synthesis, voice conversion, general diarization, etc. The environment where a person interacts with a device and input tools employed (such as phone or microphone) can impact performance. It is common to have intrinsic/extrinsic mismatch between train data and application data; in other words, data used for training the speech processing tasks is often different than those at the test time. These variations need to be considered while developing effective speech systems, especially when performance is impacted significantly due to mismatch conditions. In this dissertation, we study the problem of speaker recognition for domain mismatch. Recognizing the identity of a speaker is an important task in speaker-dependent applications, and providing robust performance regardless of how data is captured for model training and considering environmental/extrinsic changes within the application phase is very important. In this dissertation, we propose two categories of solutions to address the mismatch problem in speaker recognition: discriminant analysis based adaptation methods (generalized discriminant analysis-GDA, and support vector discriminant analysis-SVDA) and deep learning based adaptation technique (a-vector speaker embeddings). The proposed solutions are evaluated on NIST SRE-10, NIST SRE-16 and NIST SRE-18 tasks. The GDA and SVDA achieved 20% and 32% improvement in terms of EER for SRE-10 task. A-Vectors with incorporating SVDA achieved up to 18% improvement over the previous best performing solution on SRE-16 task. In addition, we propose a solution for speaker de-identification task. In more detail, the first category of solutions we propose is based on domain mismatch compensation with discriminant analysis methods. Traditional speaker recognition use linear discriminant analysis to reduce the dimensionality of speaker embeddings and provide a better discriminant feature representations for speaker classes. We propose non-linear discriminant analysis to compensate for variabilities included during recording through generalized discriminant analysis. In addition, domain adaptation is also incorporated through our proposed support vector discriminant analysis method; which also provides improved discrimination by considering the boundary structure of speaker classes. The second category of solutions are based on domain mismatch compensation with deep learning approaches. We propose a deep learning based technique to compensate for unwanted directions and information included in speaker embeddings, and provide domaininvariant speaker representations. Finally, we address speaker de- identification advancements to help protect confidential speaker or text-content within a given audio stream. Taken collectively, these three domains highlight technological advancement, which strengthen and make speaker recognition more useful in commercial, personal, and governmental/society applications, which incorporate human-speech engagement. The environment where a person interacts with a device and input tools employed (such as phone or microphone) can impact performance. It is common to have intrinsic/extrinsic mismatch between train data and application data; in other words, data used for training the speech processing tasks is often different than those at the test time. These variations need to be considered while developing effective speech systems, especially when performance is impacted significantly due to mismatch conditions. In this dissertation, we study the problem of speaker recognition for domain mismatch. Recognizing the identity of a speaker is an important task in speaker-dependent applications, and providing robust performance regardless of how data is captured for model training and considering environmental/extrinsic changes within the application phase is very important. In this dissertation, we propose two categories of solutions to address the mismatch problem in speaker recognition: discriminant analysis based adaptation methods (generalized discriminant analysis-GDA, and support vector discriminant analysis-SVDA) and deep learning based adaptation technique (a-vector speaker embeddings). The proposed solutions are evaluated on NIST SRE-10, NIST SRE-16 and NIST SRE-18 tasks. The GDA and SVDA achieved 20% and 32% improvement in terms of EER for SRE-10 task. A-Vectors with incorporating SVDA achieved up to 18% improvement over the previous best performing solution on SRE-16 task. In addition, we propose a solution for speaker de-identification task. In more detail, the first category of proposed solutions we propose are based on domain mismatch compensation with discriminant analysis methods. Traditional speaker recognition use linear discriminant analysis to reduce the dimensionality of speaker embeddings and provide a better discriminant feature representations for speaker classes. We propose non-linear discriminant analysis to compensate for variabilities included during recording through generalized discriminant analysis. In addition, domain adaptation is also incorporated through our proposed support vector discriminant analysis method; which also provides improved discrimination by considering boundary structure of speaker classes. The second category of solutions are based on domain mismatch compensation with deep learning approaches. We propose a deep learning based technique to compensate for unwanted directions and information included in speaker embeddings, and provide domain-invariant speaker representations. Finally, we address speaker de-identification advancements to help protect confidential speaker or text-content within a given audio stream. Taken collectively, these three domains highlight technological advancement, which strengthen and make speaker recognition more useful in commercial, personal, and governmental/society applications, which incorporate human-speech engagement.
Speaker Classification I
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Author : Christian Müller
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-14
Speaker Classification I written by Christian Müller and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-14 with Computers categories.
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.
Speaker Recognition
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-07-06
Speaker Recognition written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-06 with Computers categories.
What Is Speaker Recognition The identification of a person based on the features of their voice is referred to as "speaker recognition." The purpose of this information is to provide an answer to the query "Who is speaking?" Speech recognition and speaker recognition are both included in the broader concept of voice recognition. Verification of a speaker is distinct from identification of a speaker, and recognition of a speaker is not the same as diarization of a speaker. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Speaker recognition Chapter 2: Speech recognition Chapter 3: Voice analysis Chapter 4: Authentication Chapter 5: Interactive voice response Chapter 6: Biometrics Chapter 7: Electronic authentication Chapter 8: Multi-factor authentication Chapter 9: BioAPI Chapter 10: PerSay (II) Answering the public top questions about speaker recognition. (III) Real world examples for the usage of speaker recognition in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of speaker recognition' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of speaker recognition.
