Download or read book Acoustic Modeling for Efficient Speaker Verification written by Bing Xiang and published by . This book was released on 2003 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Effective Acoustic Modeling for Robust Speaker Recognition written by Taufiq Hasan Al Banna and published by . This book was released on 2013 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robustness due to mismatched train/test conditions is the biggest challenge facing the speaker recognition community today, with transmission channel and environmental noise degradation being the prominent factors. Performance of state-of-the art speaker recognition methods aim at mitigating these factors by effectively modeling speech in multiple recording conditions, so that it can learn to distinguish between inter-speaker and intra-speaker variability. The increasing demand and availability of large development corpora introduces difficulties in effective data utilization and computationally efficient modeling. Traditional compensation strategies operate on higher dimensional utterance features, known as supervectors, which are obtained from the acoustic modeling of short-time features. Feature compensation is performed during front-end processing. Motivated by the covariance structure of conventional acoustic features, we envision that feature normalization and compensation can be integrated into the acoustic modeling. In this dissertation, we investigate the following fundamental research challenges: (i) analysis of data requirements for effective and efficient background model training, (ii) introducing latent factor analysis modeling of acoustic features, (iii) integration of channel compensation strategies in mixture-models, and (iv) development of noise robust background models using factor analysis. The effectiveness of the proposed solutions are demonstrated in various noisy and channel degraded conditions using the recent evaluation datasets released by the National Institute of Standards and Technology (NIST). These research accomplishments make an important step towards improving speaker recognition robustness in diverse acoustic conditions.
Download or read book Automatic Speech and Speaker Recognition written by Joseph Keshet and published by Wiley. 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 New Paradigms for Modeling Acoustic Variation in Speech Processing written by Sina Hamidi Ghalehjegh and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "A speech signal consists of several sources of information including that associated with the sequence of phonemes in the spoken language and physiological characteristics of the speaker. Depending on the application of the speech processing system, some of this information is considered relevant to the task and some information is considered to be unwanted variability. A desired system should effectively characterize the relevant information sources, while eliminating the irrelevant variability. Fulfilling this, however, is not an easy task, because there are so many variations in acoustic conditions, speaker populations and channel conditions. As a result, there will always be an unseen context, a new speaker or an unseen environment whose characteristics are poorly represented by the system. The objective of this dissertation is to address these issues. There are four major contributions in this work.First, a technique for reducing speaker and channel variabilities is investigated in the subspace Gaussian mixture model (SGMM) framework for automatic speech recognition (ASR). The SGMM differs from the more well-known Gaussian mixture model (GMM) in that the majority of its parameters are shared across all the hidden Markov model (HMM) states and a relatively small number of parameters are state-specific. The sharing mechanism allows training ASR systems for speech datasets with limited amount of data using out-of-domain data. However, it can be problematic if the sources of data are from differing acoustic and channel conditions. An acoustic normalization technique is proposed for compensating for these sources of mismatch.Second, a two-stage speaker adaptation technique is investigated in the context of the SGMM for ASR. In the first stage, an efficient approach is presented for adapting the state-specific parameters in the SGMM. This is motivated by the study that shows state-specific parameters provide a compact and well-behaved characterization of phonetic information in the speech. In the second stage, an efficient approach is presented for a feature-space adaptation in the SGMM. Third, the use of a graph embedding framework is investigated as a regularization technique in the speaker adaptation formalism for the GMM. The technique is motivated by the fact that graph embeddings of feature vectors provide useful characterizations of the underlying manifolds on which these features lie. Incorporating these characteristics in the optimization criteria for the speaker adaptation algorithm has the effect of constraining the solution space in a way that preserves the local structure of the data. This is important, since graph embedding is generally done offline in an unsupervised manner. Therefore, large amounts of unlabeled data could potentially be used to improve the performance of the speaker adaptation technique.Finally, a technique for reducing phonetic variability is investigated for speaker verification systems. A deep neural network (DNN), trained to discriminate among speakers, is applied to improve performance in speaker verification. Features obtained from the DNN are used in an i-vector-based speaker verification system. The features derived from this network are thought to be more robust with respect to phonetic variability, which is generally considered to have a negative impact on the performance. It is found that improved performance can be obtained by appending these features to the more widely used Mel-frequency cepstrum coefficients (MFCCs)." --
Download or read book Automatic Speech and Speaker Recognition written by Chin-Hui Lee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization. Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.
Download or read book Speech Recognition written by France Mihelič and published by BoD – Books on Demand. This book was released on 2008-11-01 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes.
Download or read book Toward More Effective Acoustic Model Clustering by More Efficient Use of Data in Speech Recognition written by Chaojun Liu and published by . This book was released on 2002 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Automatic Speech Recognition written by Jinyu Li and published by Academic Press. This book was released on 2015-10-30 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications.The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided.The reader will: Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition Learn the links and relationship between alternative technologies for robust speech recognition Be able to use the technology analysis and categorization detailed in the book to guide future technology development Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years
Download or read book Subphonetic Acoustic Modeling for Speaker independent Continuous Speech Recognition written by Mei-Yuh Hwang and published by . This book was released on 1993 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book MultiMedia Modeling written by Jakub Lokoč and published by Springer Nature. This book was released on 2021-01-22 with total page 733 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 12572 and 1273 constitutes the thoroughly refereed proceedings of the 27th International Conference on MultiMedia Modeling, MMM 2021, held in Prague, Czech Republic, in June2021. Of the 211 submitted regular papers, 40 papers were selected for oral presentation and 33 for poster presentation; 16 special session papers were accepted as well as 2 papers for a demo presentation and 17 papers for participation at the Video Browser Showdown 2021. The papers cover topics such as: multimedia indexing; multimedia mining; multimedia abstraction and summarization; multimedia annotation, tagging and recommendation; multimodal analysis for retrieval applications; semantic analysis of multimedia and contextual data; multimedia fusion methods; multimedia hyperlinking; media content browsing and retrieval tools; media representation and algorithms; audio, image, video processing, coding and compression; multimedia sensors and interaction modes; multimedia privacy, security and content protection; multimedia standards and related issues; advances in multimedia networking and streaming; multimedia databases, content delivery and transport; wireless and mobile multimedia networking; multi-camera and multi-view systems; augmented and virtual reality, virtual environments; real-time and interactive multimedia applications; mobile multimedia applications; multimedia web applications; multimedia authoring and personalization; interactive multimedia and interfaces; sensor networks; social and educational multimedia applications; and emerging trends.
Download or read book Knowledge Transfer by Sharing Acoustic model Parameters for Automatic Speech Recognition written by Aanchan Mohan and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The objective of this thesis is to develop efficient methods for the transfer of knowledge between languages and speakers by sharing acoustic model parameters for automatic speech recognition (ASR). Knowledge transfer between languages is often useful when only a limited amount of transcribed data is available for ASR system development in a target language. Additionally, boot-strapping acoustic phonetic knowledge is also seen to improve ASR performance when adequate training data is available. These scenarios are used as examples to study issues in acoustic-phonetic knowledge-transfer for ASR. Furthermore, the parameters that characterize speaker variability could often be thought to lie in a low-dimensional subspace or a manifold. Parameters for a new test speaker are often estimated with knowledge transfer from training speaker information that is parametrized as a set of subspace vectors or low-dimensional embeddings on a manifold. The technical contributions in this thesis are as follows. First, acoustic mismatch due to different recording instruments and background conditions poses a problem when training a single multi-lingual statistical model on data from multiple languages. The subspace Gaussian mixture model (SGMM), which allows for natural sharing of model parameters between acoustic-phonetic units of different languages is used in this study. A two-stage procedure is proposed to compensate for speaker variability and environmental variability, prior to multi-lingual acoustic model training. As a result of this compensation procedure, ASR performance improvements are observed for all languages used in multi-lingual acoustic model training. Experimental results are presented on Hindi and Marathi speech data on a small-vocabulary agricultural commodities task. With only one hour of available Hindi data, multi-lingual acoustic model training with Marathi is seen to improve Hindi language ASR performance significantly compared to mono-lingual training. Second, to reduce the number of context-dependent errors in Hindi, an algorithm for borrowing state-level SGMM parameters from Marathi in the multi-lingual SGMM acoustic model is proposed. A statistically significant improvement is observed in Hindi language ASR. Furthermore, in order to reduce the number of parameters in the Hindi-Marathi multi-lingual acoustic model, the use of semi-tied covariance (STC) instead of full-covariance matrices is proposed. With a reduction of a factor of five relative to full-covariance parameters, similar ASR accuracy is maintained through the use of STCs. Third, the use of multi-task training for multi-lingual neural network acoustic models is studied. The use of multi-task training provides state of the art results on a well-known large vocabulary read speech task. Experiments on cross-language adaptation when only a limited amount of target language data is available are also presented. To reduce space and time-complexity to train these networks the impact of low-rank matrix factorization of the weight matrix in the final layer is presented. Finally, parameters that model speaker variability in Linear Input Network (LIN) based speaker adaptation for deep neural networks are assumed to lie on a manifold. Obtaining speaker specific parameters is treated as a task in a multi-task learning problem. Task parameters and their low-dimensional projections are assumed to lie on a manifold. A manifold constraint as a regularization term is introduced into the cost function for estimating LIN speaker parameters during test time. Experimental results are presented to evaluate this approach." --
Download or read book Voice Modeling Methods written by Thilo Stadelmann and published by Sudwestdeutscher Verlag Fur Hochschulschriften AG. This book was released on 2010-07 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building a voice model means to capture the characteristics of a speaker's voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. In this book, algorithmic and methodic improvements to the state of the art in automatic speaker recognition are presented. They are accompanied by a capacious software toolkit called "sclib." Additionally, the method of "Eidetic Design" facilitates intuitive algorithm design, development and teaching.
Download or read book Proceedings of International Conference on Data Electronics and Computing written by Nibaran Das and published by Springer Nature. This book was released on 2023-12-23 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features high-quality, peer-reviewed research papers presented at the International Conference on Data Electronics and Computing (ICDEC 2022) organized by departments of Electronics and Communication Engineering, Computer Applications, and Biomedical Engineering, North-Eastern Hill University, Shillong, Meghalaya, India during 7 – 9 September, 2022. The book covers topics in communication, networking and security, image, video and signal processing; cloud computing, IoT and smart city, AI/ML, big data and data mining, VLSI design, antenna, and microwave and control.
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 Springer Handbook of Speech Processing written by Jacob Benesty and published by Springer Science & Business Media. This book was released on 2007-11-28 with total page 1170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook plays a fundamental role in sustainable progress in speech research and development. With an accessible format and with accompanying DVD-Rom, it targets three categories of readers: graduate students, professors and active researchers in academia, and engineers in industry who need to understand or implement some specific algorithms for their speech-related products. It is a superb source of application-oriented, authoritative and comprehensive information about these technologies, this work combines the established knowledge derived from research in such fast evolving disciplines as Signal Processing and Communications, Acoustics, Computer Science and Linguistics.
Download or read book Intelligent System Design written by Suresh Chandra Satapathy and published by Springer Nature. This book was released on 2020-08-10 with total page 865 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of high-quality, peer-reviewed research papers from the 6th International Conference on Information System Design and Intelligent Applications (INDIA 2019), held at Lendi Institute of Engineering & Technology, India, from 1 to 2 November 2019. It covers a wide range of topics in computer science and information technology, including data mining and data warehousing, high-performance computing, parallel and distributed computing, computational intelligence, soft computing, big data, cloud computing, grid computing and cognitive computing.
Download or read book Recent Developments in Intelligent Computing Communication and Devices written by Srikanta Patnaik and published by Springer. This book was released on 2017-08-10 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents high quality papers presented at 2nd International Conference on Intelligent Computing, Communication & Devices (ICCD 2016) organized by Interscience Institute of Management and Technology (IIMT), Bhubaneswar, Odisha, India, during 13 and 14 August, 2016. The book covers all dimensions of intelligent sciences in its three tracks, namely, intelligent computing, intelligent communication and intelligent devices. intelligent computing track covers areas such as intelligent and distributed computing, intelligent grid and cloud computing, internet of things, soft computing and engineering applications, data mining and knowledge discovery, semantic and web technology, hybrid systems, agent computing, bioinformatics, and recommendation systems. Intelligent communication covers communication and network technologies, including mobile broadband and all optical networks that are the key to groundbreaking inventions of intelligent communication technologies. This covers communication hardware, software and networked intelligence, mobile technologies, machine-to-machine communication networks, speech and natural language processing, routing techniques and network analytics, wireless ad hoc and sensor networks, communications and information security, signal, image and video processing, network management, and traffic engineering. And finally, the third track intelligent device deals with any equipment, instrument, or machine that has its own computing capability. As computing technology becomes more advanced and less expensive, it can be built into an increasing number of devices of all kinds. The intelligent device covers areas such as embedded systems, RFID, RF MEMS, VLSI design and electronic devices, analog and mixed-signal IC design and testing, MEMS and microsystems, solar cells and photonics, nanodevices, single electron and spintronics devices, space electronics, and intelligent robotics.
