Packet Loss Concealment Algorithm for VoIP Transmission in Unreliable Networks
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
On predictive coding for erasure channels using a Kalman framework
IEEE Transactions on Signal Processing
HMM-based reconstruction of unreliable spectrographic data for noise robust speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Wireless Personal Communications: An International Journal
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As voice over IP proliferates, packet loss concealment (PLC) at the receiver has emerged as an important factor in determining voice quality of service. Through the use of heuristic variations of signal and parameter repetition and overlap-add interpolation to handle packet loss, conventional PLC systems largely ignore the dynamics of the statistical evolution of the speech signal, possibly leading to perceptually annoying artifacts. To address this problem, we propose the use of hidden Markov models for PLC. With a hidden Markov model (HMM) tracking the evolution of speech signal parameters, we demonstrate how PLC is performed within a statistical signal processing framework. Moreover, we show how the HMM is used to index a specially designed PLC module for the particular signal context, leading to signal-contingent PLC. Simulation examples, objective tests, and subjective listening tests are provided showing the ability of an HMM-based PLC built with a sinusoidal analysis/synthesis model to provide better loss concealment than a conventional PLC based on the same sinusoidal model for all types of speech signals, including onsets and signal transitions