Download or read book Comparison of Machine Learning Methods for a Speaker Recognition System written by University of Guelph. School of Engineering and published by . This book was released on 2009 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book 2020 6th International Conference on Advanced Computing and Communication Systems ICACCS written by IEEE Staff and published by . This book was released on 2020-03-06 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 2020 International Conference on Advanced Computing & Communication Systems (ICACCS) aims at exploring the interface between the industry and real time environment with state of the art techniques ICACCS 2020 publishes original and timely research papers and survey articles in current areas of sustainable computing, energy, smart city, temperature, power and environment related research areas of current importance to readers

Download or read book Machine Learning for Speaker Recognition written by Man-Wai Mak and published by Cambridge University Press. This book was released on 2020-11-19 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Automatic Speech and Speaker Recognition written by Joseph Keshet and published by John Wiley & Sons. This book was released on 2009-04-27 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: Provides an up-to-date snapshot of the current state of research in this field Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.

Download or read book Speaker Classification I written by Christian Müller and published by Springer. This book was released on 2007-08-28 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Neural Network Based Representation Learning and Modeling for Speech and Speaker Recognition written by Jinxi Guo and published by . This book was released on 2019 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning and neural network research has grown significantly in the fields of automatic speech recognition (ASR) and speaker recognition. Compared to traditional methods, deep learning-based approaches are more powerful in learning representation from data and building complex models. In this dissertation, we focus on representation learning and modeling using neural network-based approaches for speech and speaker recognition. In the first part of the dissertation, we present two novel neural network-based methods to learn speaker-specific and phoneme-invariant features for short-utterance speaker verification. We first propose to learn a spectral feature mapping from each speech signal to the corresponding subglottal acoustic signal which has less phoneme variation, using deep neural networks (DNNs). The estimated subglottal features show better speaker-separation ability and provide complementary information when combined with traditional speech features on speaker verification tasks. Additional, we propose another DNN-based mapping model, which maps the speaker representation extracted from short utterances to the speaker representation extracted from long utterances of the same speaker. Two non-linear regression models using an autoencoder are proposed to learn this mapping, and they both improve speaker verification performance significantly. In the second part of the dissertation, we design several new neural network models which take raw speech features (either complex Discrete Fourier Transform (DFT) features or raw waveforms) as input, and perform the feature extraction and phone classification jointly. We first propose a unified deep Highway (HW) network with a time-delayed bottleneck layer (TDB), in the middle, for feature extraction. The TDB-HW networks with complex DFT features as input provide significantly lower error rates compared with hand-designed spectrum features on large-scale keyword spotting tasks. Next, we present a 1-D Convolutional Neural Network (CNN) model, which takes raw waveforms as input and uses convolutional layers to do hierarchical feature extraction. The proposed 1-D CNN model outperforms standard systems with hand-designed features. In order to further reduce the redundancy of the 1-D CNN model, we propose a filter sampling and combination (FSC) technique, which can reduce the model size by 70% and still improve the performance on ASR tasks. In the third part of dissertation, we propose two novel neural-network models for sequence modeling. We first propose an attention mechanism for acoustic sequence modeling. The attention mechanism can automatically predict the importance of each time step and select the most important information from sequences. Secondly, we present a sequence-to-sequence based spelling correction model for end-to-end ASR. The proposed correction model can effectively correct errors made by the ASR systems.

Download or read book Deep Learning for Speech Classification and Speaker Recognition written by Muhammad Muneeb Saleem and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Privacy Preserving Machine Learning for Speech Processing written by Manas A. Pathak and published by Springer Science & Business Media. This book was released on 2012-10-25 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Doctoral Thesis accepted by Carnegie Mellon University, USA"--Title page.

Download or read book New Era for Robust Speech Recognition written by Shinji Watanabe and published by Springer. This book was released on 2017-10-30 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.

Download or read book Fundamentals of Speaker Recognition written by Homayoon Beigi and published by Springer Science & Business Media. This book was released on 2011-12-09 with total page 984 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Handbook of Research on Emerging Trends and Applications of Machine Learning written by Solanki, Arun and published by IGI Global. This book was released on 2019-12-13 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.

Download or read book Automatic Speech Recognition written by Dong Yu and published by Springer. This book was released on 2014-11-11 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

Download or read book Handbook of Machine Learning for Computational Optimization written by Vishal Jain and published by CRC Press. This book was released on 2021-11-02 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.

Download or read book Deep Learning Based Methods for Detection Separation and Recognition of Overlapping Speech written by Midia Yousefi and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: All speech technology systems such as Automatic Speech Recognition (ASR), speaker diarization, speaker recognition/verification, and speech synthesis have advanced significantly since the emergence of deep learning techniques. However, the performance of these voice-enable systems degrades rapidly in non-ideal naturalistic environmental circumstances, specifically with the existence of an interfering talker. This challenge, known as the cocktail party problem is a psycho-acoustic phenomena, which refers to the remarkable ability of the human auditory system to selectively attend, recognize and extract meaningful information out of the complex auditory signal in a noisy environment, where the interfering sounds are produced by competing talkers or a variety of noises. For humans, thus perceptual processing is made possible due to bilateral hearing, where for speech technology, single-channel audio streams do not allow for any directional sound processing. However, even for humans with normal hearing abilities, the capacity of the human auditory system to extract and separate simultaneous sources from a mixture is severely compromised. In this dissertation, we propose novel approaches for designing algorithms to detect, separate, and recognize overlapping speech signals as well as extracting higher level information from multi-talker speech segments to reduce the existing gap between real world naturalistic environmental circumstances and current automatic speech technology systems. Specifically, we propose (i) three alternate Convolutional Neural Networks (CNN) models for detection of overlapping speech for segment turns as short as 25 msec, (ii) an attention-based (CNN) architecture, which attends to different sound sources in order to count the number of active speakers, and (iii) formulation of a Probabilistic Permutation Invariant Training framework to optimize and train a Long-Short Term Memory (LSTM) network to estimate the speaker-specific speech signals from a single channel mixed audio recording. Next, we develop a hybrid DNN/HMM speech recognition system to identify and recognize a desired speaker. Experimental results are provided based on simulated overlapping speech signals based on the WSJ, TIMIT, and GRID datasets, which demonstrate the effectiveness of the proposed approaches for processing overlapping speech signals. The experimental results highlight the capability of the proposed system in detecting overlapping speech frames with 90.5% accuracy, 93.5% precision, 92.7% recall, and a 92.8% F-score on the GRID dataset. Also, experimental results on TIMIT and GRID datasets show that the proposed Prob-PIT speech separation system significantly outperforms the conventional PIT benchmark in terms of Signal-to-Distortion Ratio (SDR) and Signal-to-Interference Ratio (SIR). The proposed ASR system provides an absolute Word-Error-Rate (WER) improvement of +7% with respect to a conventional ASR system trained without using speaker-specific information. Taken collectively, the advancements on overlapping speech detection, speaker count estimation in multi-speaker scenarios, and speech separation based on probabilistic permutation invariant training have provided important technological improvement to improve speech technology solutions for naturalistic speech scenarios.

Download or read book Machine Learning for Speaker Recognition written by Man-Wai Mak and published by Cambridge University Press. This book was released on 2020-11-19 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn fundamental and advanced machine learning techniques for robust speaker recognition and domain adaptation with this useful toolkit.

Download or read book Deep Learning for NLP and Speech Recognition written by Uday Kamath and published by Springer. This book was released on 2019-06-10 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

Download or read book Proceedings of the 12th International Conference on Soft Computing for Problem Solving written by Millie Pant and published by Springer Nature. This book was released on with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: