Principles of Speech Coding

Chapter
Part of the Springer Handbooks book series (SPRINGERHAND)

Abstract

Speech coding is the art of reducing the bit rate required to describe a speech signal. In this chapter, we discuss the attributes of speech coders as well as the underlying principles that determine their behavior and their architecture. The ubiquitous class of linear-prediction-based coders is used as an illustration. Speech is generally modeled as a sequence of stationary signal segments, each having unique statistics. Segments are encoded using a two-step procedure: (1) find a model describing the speech segment, (2) encode the segment assuming it is generated by the model. We show that the bit allocation for the model (the predictor parameters) is independent of overall rate and of perception, which is consistent with existing experimental results. The modeling of perception is an important aspect of efficient coding and we discuss how various perceptual distortion measures can be integrated into speech coders.

Keywords

Speech Signal Autoregressive Model Transmission Control Protocol Mean Opinion Score Speech Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

AMR-WB

wide-band AMR speech coder

AR

autoregressive

DCT

discrete cosine transform

DFT

discrete Fourier transform

ERB

equivalent rectangular bandwidth

IP

internet protocol

JND

just-noticeable difference

LSF

line spectral frequency

MOS

mean opinion score

OSI

open systems interconnection reference

TCP

transmission control protocol

UDP

user datagram protocol

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.School of Electrical Engineering, Sound and Image Processing LabRoyal Institute of Technology (KTH)StockholmSweden

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