New Advances In Voice Activity Detection Using Hos And Optimization Strategies PDF Download
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Author | : J.M. Gorriz |
Publisher | : |
Total Pages | : |
Release | : 2007 |
Genre | : |
ISBN | : 9783902613080 |
Download New Advances in Voice Activity Detection Using HOS and Optimization Strategies Book in PDF, ePub and Kindle
This paper showed three different schemes for improving speech detection robustness and the performance of speech recognition systems working in noisy environments. These methods are based on: i) statistical likelihood ratio tests (LRTs) formulated in terms of the integrated bispectrum of the noisy signal. The integrated bispectrum is defined as a cross spectrum between the signal and its square, and therefore a function of a single frequency variable. It inherits the ability of higher order statistics to detect signals in noise with many other additional advantages; ii) Hard decision clustering approach where a set of prototypes is used to characterize the noisy channel. Detecting the presence of speech is enabled by a decision rule formulated in terms of an averaged distance between the observation vector and a cluster-based noise model; and iii) an effective method employing support vector machines (SVM) , a paradigm of learning from examples based in Vapkik-Chervonenkis theory. The use of kernels in SVM enables to map the data, via a nonlinear transformation, into some other dot product space (called feature space) in which the classification task is settled. The proposed methods incorporate contextual information to the decision rule, a strategy that has reported significant improvements in speech detection accuracy and robust speech recognition applications. The optimal window size was determined by analyzing the overlap between the distributions of the decision variable and the error rate. The experimental analysis conducted on the well-known AURORA databases has reported significant improvements over standardized techniques such as ITU G.729, AMR1, AMR2 and ESTI AFE VADs, as well as over recently published VADs. The analysis assessed: i) the speech/non-speech detection accuracy by means of the ROC curves, with the proposed VADs yielding improved hit-rates and reduced false alarms when compared to all the reference algorithms, and ii) the recognition rate when the VADs are considered as part of a complete speech recognition system, showing a sustained advantage in speech recognition performance.
Author | : Michael Grimm |
Publisher | : BoD – Books on Demand |
Total Pages | : 471 |
Release | : 2007-06-01 |
Genre | : Computers |
ISBN | : 3902613084 |
Download Robust Speech Book in PDF, ePub and Kindle
This book on Robust Speech Recognition and Understanding brings together many different aspects of the current research on automatic speech recognition and language understanding. The first four chapters address the task of voice activity detection which is considered an important issue for all speech recognition systems. The next chapters give several extensions to state-of-the-art HMM methods. Furthermore, a number of chapters particularly address the task of robust ASR under noisy conditions. Two chapters on the automatic recognition of a speaker's emotional state highlight the importance of natural speech understanding and interpretation in voice-driven systems. The last chapters of the book address the application of conversational systems on robots, as well as the autonomous acquisition of vocalization skills.
Author | : Sameeraj Meduri |
Publisher | : LAP Lambert Academic Publishing |
Total Pages | : 52 |
Release | : 2012-07 |
Genre | : |
ISBN | : 9783659172045 |
Download A Survey and Evaluation of Voice Activity Detection Algorithms Book in PDF, ePub and Kindle
With the recent advances in speech signal processing techniques, the need to detect the presence of speech accurately in the incoming signal under different noise environments has become a major concern of the industry. The separation of speech segment from the non-speech segment in an audio signal is achieved using a Voice Activity Detectors (VAD). VAD's are a class signal processing methods that detects the presence or absence of speech in short segments of audio signal. A VAD has a pivotal role as a preprocessing block in wide range of speech applications. An integrated VAD in speech communication system, improves channel capacity, reduces co-channel interference and power consumption in portable electronic devices in cellular radio systems and allows simultaneous voice and data applications in multimedia communications. In slowly varying non-stationary environments where speech is corrupted by noise, a VAD is used to learn noise characteristics and estimate the noise spectrum. Furthermore, the output from the VAD is helpful in improving the performance of the speech recognition systems which applies a technique called non-speech frame dropping (FD) to reduce the insertion error
Author | : Fei Tao (Electrical engineer) |
Publisher | : |
Total Pages | : |
Release | : 2018 |
Genre | : Automatic speech recognition |
ISBN | : |
Download Advances in Audiovisual Speech Processing for Robust Voice Activity Detection and Automatic Speech Recognition Book in PDF, ePub and Kindle
Speech processing systems are widely used in existing commercial applications, including virtual assistants in smartphones and home assistant devices. Speech-based commands provide convenient hands-free functionality for users. Two key speech processing systems in practical applications are voice activity detection (VAD), which aims to detect when a user is speaking to a system, and automatic speech recognition (ASR), which aims to recognize what the user is speaking. A limitation in these speech tasks is the drop in performance observed in noisy environments or when the speech mode differs from neutral speech (e.g., whisper speech). Emerging audiovisual solutions provide principled frameworks to increase the robustness of the systems by incorporating features describing lip motion. This study proposes novel audiovisual solutions for VAD and ASR tasks. The dissertation introduces unsupervised and supervised audiovisual voice activity detection (AV-VAD). The unsupervised approach combines visual features that are characteristic of the semi-periodic nature of the articulatory production around the orofacial area. The visual features are combined using principal component analysis (PCA) to obtain a single feature. The threshold between speech and non-speech activity is automatically estimated with the expectation-maximization (EM) algorithm. The decision boundary is improved by using the Bayesian information criterion (BIC) algorithm, resolving temporal ambiguities caused by different sampling rates and anticipatory movements. The supervised framework corresponds to the bimodal recurrent neural network (BRNN), which captures the taskrelated characteristics in the audio and visual inputs, and models the temporal information within and across modalities. The approach relied on three subnetworks implemented with long short-term memory (LSTM) networks. This framework is implemented with either hand-crafted features or features representations directly derived from the data (i.e., end-toend system). The study also extends this framework by increasing the temporal modeling by using advanced LSTMs (A-LSTMs). For audiovisual automatic speech recognition (AV-ASR), the study explores the use of visual features to compensate for the mismatch observed when the system is evaluated with whisper speech. We propose supervised adaptation schemes which significantly reduce the mismatch between normal and whisper speech across speakers. The study also introduces the Gating neural network (GNN). The GNN aims to attenuate the effect of unreliable features, creating AV-ASR systems that improve, or at least maintain, the performance of an ASR system implemented only with speech. Finally, the dissertation introduces the front-end alignment neural network (AliNN) to address the temporal alignment problem between audio and visual features. This front-end system is important as the lip motion often precedes speech (e.g., anticipatory movements). The framework relies on RNN with attention model. The resulting aligned features are concatenated and fed to conventional back-end ASR systems obtaining performance improvements. The proposed approaches for AV-VAD and AV-ASR systems are evaluated on large audiovisual corpora, achieving competitive performance under real world scenarios, outperforming conventional audio-based VAD and ASR systems or alternative audiovisual systems proposed by previous studies. Taken collectively, this dissertation has made algorithmic advancements for audiovisual systems, representing novel contributions to the field of multimodal processing.
Author | : Matthew McEachern |
Publisher | : |
Total Pages | : 90 |
Release | : 2018 |
Genre | : |
ISBN | : |
Download Neural Voice Activity Detection and Its Practical Use Book in PDF, ePub and Kindle
The task of producing a Voice Activity Detector (VAD) that is robust in the presence of non-stationary background noise has been an active area of research for several decades. Historically, many of the proposed VAD models have been highly heuristic in nature. More recently, however, statistical models, including Deep Neural Networks (DNNs) have been explored. In this thesis, I explore the use of a lightweight, deep, recurrent neural architecture for VAD. I also explore a variant that is fully end-to-end, learning features directly from raw waveform data. In obtaining data for these models, I introduce a data augmentation methodology that allows for the artificial generation of large amounts of noisy speech data from a clean speech source. I describe how these neural models, once trained, can be deployed in a live environment with a real-time audio stream. I find that while these models perform well in their closed-domain testing environment, the live deployment scenario presents challenges related to generalizability.
Author | : Maciej Huk |
Publisher | : Springer |
Total Pages | : 438 |
Release | : 2019-03-05 |
Genre | : Technology & Engineering |
ISBN | : 3030141322 |
Download Intelligent Information and Database Systems: Recent Developments Book in PDF, ePub and Kindle
This book presents research reports selected to indicate the state of the art in intelligent and database systems and to promote new research in this field. It includes 34 chapters based on original research presented as posters at the 11th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2019), held in Yogyakarta, Indonesia on 8–11 April 2019. The increasing use of intelligent and database systems in various fields, such as industry, medicine and science places those two elements of computer science among the most important directions of research and application, which currently focuses on such key technologies as machine learning, cloud computing and processing of big data. It is estimated that further development of intelligent systems and the ability to gather, store and process enormous amounts of data will be needed to solve a number of crucial practical and theoretical problems. The book is divided into five parts: (a) Sensor Clouds and Internet of Things, (b) Machine Learning and Decision Support Systems, (c) Computer Vision Techniques and Applications, (d) Intelligent Systems in Biomedicine, and (e) Applications of Intelligent Information Systems. It is a valuable resource for researchers and practitioners interested in increasing the synergy between artificial intelligence and database technologies, as well as for graduate and Ph.D. students in computer science and related fields.
Author | : Rameez Ahmad |
Publisher | : |
Total Pages | : 72 |
Release | : 2015 |
Genre | : Automatic speech recognition |
ISBN | : |
Download On Improving Voice Activity Detection Using Unsupervised Visual Speech Detector Book in PDF, ePub and Kindle
Author | : Valentina E. Balas |
Publisher | : Springer Nature |
Total Pages | : 340 |
Release | : 2022-02-14 |
Genre | : Technology & Engineering |
ISBN | : 303090119X |
Download Recent Advances in Internet of Things and Machine Learning Book in PDF, ePub and Kindle
This book covers a domain that is significantly impacted by the growth of soft computing. Internet of Things (IoT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of IoT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for IoT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to IoT and machine learning. In this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of IoT and machine learning.
Author | : Zhihua Cai |
Publisher | : Springer |
Total Pages | : 551 |
Release | : 2010-10-14 |
Genre | : Computers |
ISBN | : 3642164935 |
Download Advances in Computation and Intelligence Book in PDF, ePub and Kindle
Volumes CCIS 107 and LNCS 6382 constitute the proceedings of the 5th International Symposium, ISICA 2010, held in Wuhan, China, in October 2010. ISICA 2010 attracted 267 submissions and through rigorous reviews 53 papers were included in LNCS 6382. The papers are presented in sections on ANT colony and particle swarm optimization, differential evolution, distributed computing, genetic algorithms, multi-agent systems, multi-objective and dynamic optimization, robot intelligence, statistic learning and system design.
Author | : Ewa Pietka |
Publisher | : Springer |
Total Pages | : 615 |
Release | : 2018-06-05 |
Genre | : Technology & Engineering |
ISBN | : 3319912119 |
Download Information Technology in Biomedicine Book in PDF, ePub and Kindle
ITiB’2018 is the 6th Conference on Information Technology in Biomedicine, hosted every two years by the Department of Informatics & Medical Devices, Faculty of Biomedical Engineering, Silesian University of Technology. The Conference is organized under the auspices of the Committee on Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. The meeting has become an established event that helps to address the demand for fast and reliable technologies capable of processing data and delivering results in a user-friendly, timely and mobile manner. Many of these areas are recognized as research and development frontiers in employing new technology in the clinical setting. Technological assistance can be found in prevention, diagnosis, treatment, and rehabilitation alike. Homecare support for any type of disability may improve standard of living and make people’s lives safer and more comfortable. The book includes the following sections: Ø Image Processing Ø Multimodal Imaging and Computer-aided Surgery Ø Computer-aided Diagnosis Ø Signal Processing and Medical Devices Ø Bioinformatics Ø Modelling & Simulation Ø Analytics in Action on the SAS Platform Ø Assistive Technologies and Affective Computing (ATAC)