Böcker i SpringerBriefs in Speech Technology-serien

This book discusses digital audio watermarking copyright assurance. The author first outlines the topic of watermarking data that can be used for copyright assurance that incorporates text messages, copyright audio, handwritten text, logo and cell phone numbers.

This book explains speech enhancement in the Fractional Fourier Transform (FRFT) domain and investigates the use of different FRFT algorithms in both single channel and multi-channel enhancement systems, which has proven to be an ideal time frequency analysis tool in many speech signal processing applications.

This book provides a survey on wide-spread of employing wavelets analysis in different applications of speech processing. The author examines development and research in different applications of speech processing. The book also summarizes the state of the art research on wavelet in speech processing.

This book introduces audio watermarking methods in transform domain based on matrix decomposition for copyright protection. Chapter 1 discusses the application and properties of digital watermarking. Chapter 2 proposes a blind lifting wavelet transform (LWT) based watermarking method using fast Walsh Hadamard transform (FWHT) and singular value decomposition (SVD) for audio copyright protection. Chapter 3 presents a blind audio watermarking method based on LWT and QR decomposition (QRD) for audio copyright protection. Chapter 4 introduces an audio watermarking algorithm based on FWHT and LU decomposition (LUD). Chapter 5 proposes an audio watermarking method based on LWT and Schur decomposition (SD). Chapter 6 explains in details on the challenges and future trends of audio watermarking in various application areas.Introduces audio watermarking methods for copyright protection and ownership protection;Describes watermarking methods with encryption and decryption that provide excellent performance in terms of imperceptibility, robustness, and data payload;Discusses in details on the challenges and future research direction of audio watermarking in various application areas.

The performance of the proposed method is evaluated using various transform bases like Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Fast Discrete Curvelet Transform (FDCuT).

This book covers language modeling and automatic speech recognition for inflective languages (e.g. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications.

This book introduces audio watermarking methods for copyright protection, which has drawn extensive attention for securing digital data from unauthorized copying.

Each chapter provides the motivation for exploring the specific feature for SR task, discusses the methods to extract those features, and finally suggests appropriate models to capture the sound unit specific knowledge from the proposed features.

This book discusses the Partially Observable Markov Decision Process (POMDP) framework applied in dialogue systems. Starting from scratch, they present the state, the transition model, the observation model and then finally the reward model from unannotated and noisy dialogues.

The book discusses how in implicit processing approach, excitation source features are derived from LP residual, Hilbert envelope (magnitude) of LP residual and Phase of LP residual;

Contemporary Methods for Speech Parameterization offers a general view of short-time cepstrum-based speech parameterization and provides a common ground for further in-depth studies on the subject.

This book explains how can be created information extraction (IE) applications that are able to tap the vast amount of relevant information available in natural language sources: Internet pages, official documents such as laws and regulations, books and newspapers, and social web.

This book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.

This book focuses on speech processing in the presence of low-bit rate coding and varying background environments. Accurate estimation of these crucial events will be useful for carrying out various speech tasks such as speech recognition, speaker recognition and speech rate modification in mobile environments.

Advances in Non-Linear Modeling for Speech Processing includes advanced topics in non-linear estimation and modeling techniques along with their applications to speaker recognition.