Robust Automatic Speech Recognition

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

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  • Author : Jinyu Li
  • Publisher : Academic Press
  • Pages : 306 pages
  • ISBN : 0128026162
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKRobust Automatic Speech Recognition

Robust Automatic Speech Recognition

Robust Automatic Speech Recognition
  • Author : Jinyu Li,Li Deng,Reinhold Haeb-Umbach,Yifan Gong
  • Publisher : Academic Press
  • Release : 30 October 2015
GET THIS BOOKRobust Automatic Speech Recognition

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

Techniques for Noise Robustness in Automatic Speech Recognition

Techniques for Noise Robustness in Automatic Speech Recognition
  • Author : Tuomas Virtanen,Rita Singh,Bhiksha Raj
  • Publisher : John Wiley & Sons
  • Release : 19 September 2012
GET THIS BOOKTechniques for Noise Robustness in Automatic Speech Recognition

Automatic speech recognition (ASR) systems are findingincreasing use in everyday life. Many of the commonplaceenvironments where the systems are used are noisy, for exampleusers calling up a voice search system from a busy cafeteria or astreet. This can result in degraded speech recordings and adverselyaffect the performance of speech recognition systems. As theuse of ASR systems increases, knowledge of the state-of-the-art intechniques to deal with such problems becomes critical to systemand application engineers and researchers who work with or on

Robustness in Automatic Speech Recognition

Robustness in Automatic Speech Recognition
  • Author : Jean-Claude Junqua,Jean-Paul Haton
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKRobustness in Automatic Speech Recognition

Foreword Looking back the past 30 years. we have seen steady progress made in the area of speech science and technology. I still remember the excitement in the late seventies when Texas Instruments came up with a toy named "Speak-and-Spell" which was based on a VLSI chip containing the state-of-the-art linear prediction synthesizer. This caused a speech technology fever among the electronics industry. Particularly. applications of automatic speech recognition were rigorously attempt ed by many companies. some of which were start-ups

Robust Automatic Speech Recognition Employing Phoneme-dependent Multi-environment Enhanced Models Based Linear Normalization

Robust Automatic Speech Recognition Employing Phoneme-dependent Multi-environment Enhanced Models Based Linear Normalization
  • Author : Igmar Hernández Ochoa
  • Publisher : Unknown Publisher
  • Release : 25 January 2021
GET THIS BOOKRobust Automatic Speech Recognition Employing Phoneme-dependent Multi-environment Enhanced Models Based Linear Normalization

This work shows a robust normalization technique by cascading a speech enhance-ment method followed by a feature vector normalization algorithm. An efficient scheme used to provide speech enhancement is the Spectral Subtraction algorithm, which reduces the effect of additive noise by performing a subtraction of noise spectrum estimate over the complete speech spectrum. On the other hand, a new and promising technique known as PD-MEMLIN (Phoneme-Dependent Multi-Enviroment Models based Linear Normalization) has also shown to be effective. PD-MEMLIN is an

New Era for Robust Speech Recognition

New Era for Robust Speech Recognition
  • Author : Shinji Watanabe,Marc Delcroix,Florian Metze,John R. Hershey
  • Publisher : Springer
  • Release : 30 October 2017
GET THIS BOOKNew Era for Robust Speech Recognition

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

Neural Network Projects with Python

Neural Network Projects with Python
  • Author : James Loy
  • Publisher : Packt Publishing Ltd
  • Release : 28 February 2019
GET THIS BOOKNeural Network Projects with Python

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI Build expert neural networks in Python using popular libraries such as Keras Includes projects such as object detection, face identification, sentiment analysis, and more Book Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including

Robust Speech Recognition of Uncertain or Missing Data

Robust Speech Recognition of Uncertain or Missing Data
  • Author : Dorothea Kolossa,Reinhold Haeb-Umbach
  • Publisher : Springer Science & Business Media
  • Release : 14 July 2011
GET THIS BOOKRobust Speech Recognition of Uncertain or Missing Data

Automatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by using not only a preprocessed speech signal but also an estimate of its reliability to selectively focus on those segments and features that are most reliable for recognition. This book presents the state of the art in recognition in the presence of

Acoustical and Environmental Robustness in Automatic Speech Recognition

Acoustical and Environmental Robustness in Automatic Speech Recognition
  • Author : A. Acero
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKAcoustical and Environmental Robustness in Automatic Speech Recognition

The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more systems are finding their way into practical applications. Although the issue of environmental robustness has received only a small fraction of the attention devoted to speaker independence, even speech recognition systems that are designed to be speaker independent frequently perform very poorly when they are tested using a different type of microphone

Robust Automatic Speech Recognition and Moduling of Auditory Discrimination with Auditory Experiments Spectro-temporal Features

Robust Automatic Speech Recognition and Moduling of Auditory Discrimination with Auditory Experiments Spectro-temporal Features
  • Author : Marc René Schädler
  • Publisher : Unknown Publisher
  • Release : 25 January 2021
GET THIS BOOKRobust Automatic Speech Recognition and Moduling of Auditory Discrimination with Auditory Experiments Spectro-temporal Features

Automatic speech recognition (ASR) systems still do not perform as well as human listeners under realistic conditions. The unmatched ability of humans to understand speech in most difficult acoustic conditions originates from the superior properties of their auditory system. The aim of this thesis is to improve the recognition performance of ASR systems in difficult acoustic conditions by carefully integrating auditory signal processing strategies. To this end, the physiologically inspired extraction of spectro-temporal modulation patterns was successfully integrated into the

Automatic Speech and Speaker Recognition

Automatic Speech and Speaker Recognition
  • Author : Chin-Hui Lee,Frank K. Soong,Kuldip K. Paliwal
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKAutomatic Speech and Speaker Recognition

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

Robust Automatic Recognition of Birdsongs and Human Speech: a Template-Based Approach

Robust Automatic Recognition of Birdsongs and Human Speech: a Template-Based Approach
  • Author : Kantapon Kaewtip
  • Publisher : Unknown Publisher
  • Release : 25 January 2021
GET THIS BOOKRobust Automatic Recognition of Birdsongs and Human Speech: a Template-Based Approach

This dissertation focuses on robust signal processing algorithms for birdsongs and speech signals. Automatic phrase or syllable detection systems of bird sounds are useful in several applications. However, bird-phrase detection is challenging due to segmentation error, duration variability, limited training data, and background noise. Two spectrograms with identical class labels may look different due to time misalignment and frequency variation. In real recording environments such as in a forest, the data can be corrupted by background interference, such as rain,

Robust Speech Recognition and Understanding

Robust Speech Recognition and Understanding
  • Author : Danel Jaso
  • Publisher : Unknown Publisher
  • Release : 01 April 2016
GET THIS BOOKRobust Speech Recognition and Understanding

"Speech recognition systems have become much more robust in recent years with respect to both speaker variability and acoustical variability. Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. In addition to achieving

Distant Speech Recognition

Distant Speech Recognition
  • Author : Matthias Woelfel,John McDonough
  • Publisher : John Wiley & Sons
  • Release : 20 April 2009
GET THIS BOOKDistant Speech Recognition

A complete overview of distant automatic speech recognition The performance of conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker. This is due to a broad variety of effects such as background noise, overlapping speech from other speakers, and reverberation. While traditional ASR systems underperform for speech captured with far-field sensors, there are a number of novel techniques within the recognition system as well as techniques developed

Robust Speech

Robust Speech
  • Author : Michael Grimm,Kristian Kroschel
  • Publisher : BoD – Books on Demand
  • Release : 01 June 2007
GET THIS BOOKRobust Speech

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

Recent Advances in Robust Speech Recognition Technology

Recent Advances in Robust Speech Recognition Technology
  • Author : Javier Ramírez,Juan Manuel Górriz
  • Publisher : Bentham Science
  • Release : 01 January 2011
GET THIS BOOKRecent Advances in Robust Speech Recognition Technology

This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refers to the need to maintain high speech recognition accuracy even when the quality of the input speech is degraded, or when the acoustical, articulate, or phonetic characteristics of speech in the training and testing environments differ. Obstacles to robust recognition include acoustical degradations produced by additive noise, the effects of linear filtering, nonlinearities in transduction or transmission, as well as impulsive