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Fundamentals of speech recognition
This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech. Expand
Hidden Markov Models for Speech Recognition
The role of statistical methods in this powerful technology as applied to speech recognition is addressed and a range of theoretical and practical issues that are as yet unsolved in terms of their importance and their effect on performance for different system implementations are discussed. Expand
Minimum classification error rate methods for speech recognition
The issue of speech recognizer training from a broad perspective with root in the classical Bayes decision theory is discussed, and the superiority of the minimum classification error (MCE) method over the distribution estimation method is shown by providing the results of several key speech recognition experiments. Expand
Speech Dereverberation Based on Variance-Normalized Delayed Linear Prediction
NDLP can robustly estimate an inverse system for late reverberation in the presence of noise without greatly distorting a direct speech signal and can be implemented in a computationally efficient manner in the time-frequency domain. Expand
Discriminative learning for minimum error classification [pattern recognition]
A fundamental technique for designing a classifier that approaches the objective of minimum classification error in a more direct manner than traditional methods is given and is contrasted with several traditional classifier designs in typical experiments to demonstrate the superiority of the new learning formulation. Expand
Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
The proposed deep learning-based approach to handle wireless OFDM channels in an end-to-end manner is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists. Expand
Line spectrum pair (LSP) and speech data compression
An expression for spectral sensitivity with respect to single LSP frequency deviation is derived such that some insight on their quantization effects can be obtained and results on multi-pulse LPC using LSP for spectral information compression are presented. Expand
Signal Processing in Cognitive Radio
The fundamental signal-processing aspects involved in developing a fully functional cognitive radio network, including spectrum sensing and spectrum sculpting are described. Expand
The segmental K-means algorithm for estimating parameters of hidden Markov models
  • B. Juang, L. Rabiner
  • Computer Science
  • IEEE Trans. Acoust. Speech Signal Process.
  • 1 September 1990
The authors discuss and document a parameter estimation algorithm for data sequence modeling involving hidden Markov models that uses the state-optimized joint likelihood for the observation data and the underlying Markovian state sequence as the objective function for estimation. Expand
Discriminative Learning for Minimum Error Classification
Recently, due to the advent of artificial neural networks and learning vector quantizers, there is a resurgent interest in reexamining the classical techniques of discriminant analysis to suit theExpand