Discrete-Time Processing of Speech Signals

  title={Discrete-Time Processing of Speech Signals},
  author={John Robert Deller and John G. Proakis and John H. L. Hansen},
Preface to the IEEE Edition. Preface. Acronyms and Abbreviations. SIGNAL PROCESSING BACKGROUND. Propaedeutic. SPEECH PRODUCTION AND MODELLING. Fundamentals of Speech Science. Modeling Speech Production. ANALYSIS TECHNIQUES. Short--Term Processing of Speech. Linear Prediction Analysis. Cepstral Analysis. CODING, ENHANCEMENT AND QUALITY ASSESSMENT. Speech Coding and Synthesis. Speech Enhancement. Speech Quality Assessment. RECOGNITION. The Speech Recognition Problem. Dynamic Time Warping. The… Expand
Speech Production Modeling and Analysis
This chapter outlines how the vocal tract model parameters can be estimated by using the autocorrelation- and covariance methods and the estimation of voice source signal and its modeling is explained. Expand
Wavelet-based energy binning cepstral features for automatic speech recognition
This paper analyses cepstral representation in the context of the synchrosqueezed representation wastrum and discusses energy accumulation derived wastra as opposed to classicalMEL and LPC derived cepstra. Expand
Segmentation and its real-world applications in speech processing
A new time-scale transform based segmentation method is presented and it is found that the method is able to extract speech components from non-speech components effectively and is successfully applied to insert selective pauses in the speech before delivering in the reverberant environment and improve the quality/intelligibility of the delivered speech. Expand
Pitch determination and speech segmentation using the discrete wavelet transform
  • C. Wendt, A. Petropulu
  • Computer Science
  • 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96
  • 1996
A time-based event detection method based on the discrete wavelet transform that detects voiced speech, which is local in frequency, and determines the pitch period, and it is shown that it is both accurate and robust to noise. Expand
Spectral Analysis in Speech Processing Techniques Prof . Vijaya Sugandhi
The corruption of speech due to presence of additive background noise causes severe difficulties in various communication environments. This paper addresses the problem of reduction of additiveExpand
Discrete-Time Speech Signal Processing: Principles and Practice
This chapter discusses the Discrete-Time Speech Signal Processing Framework, a model based on the FBS Method, and its applications in Speech Communication Pathway and Homomorphic Signal Processing. Expand
Speech Analysis and Synthesis Based on Dynamic Modes
A framework based on dynamic mode predictors and filters is presented, which are adapted, using gradient-based techniques, to track the modal dynamics of speech yielding a representation which is free from quasi-stationary assumptions thus allowing flexible manipulation of the speech signal. Expand
Voice activity detection Algorithm for Speech Recognition Applications
This paper is concerned with labeling sections of speech samples based on whether they are silence, voiced or unvoiced speech using calculations over the speech samples; zero crossing and short-term energy functions. Expand
Post-Processing Method for Single Channel Speech Enhancement Systems
Boosting the performance of a conventional speech enhancement system by applying post-processing restoration modules. The speech production process is modeled with linear prediction analysis (LPA).Expand
Signal modeling techniques in speech recognition
A tutorial on signal processing in state-of-the-art speech recognition systems is presented, reviewing those techniques most commonly used, and three important trends that have developed in the last five years in speech recognition are examined. Expand