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Signal models for low bit-rate coding of speech

Authors: J. L. Flanagan; K. Ishizaka; K. L. Shipley;

Signal models for low bit-rate coding of speech

Abstract

A traditional model of the speech signal has provided the underpinning of vocoder technology since the inception of analysis/synthesis telephony. The model is a first-order approximation to human speech generation in which the source of vocal sound and the resonant acoustic system are treated as linear, separable elements. This source-system model cannot properly account for a number of acoustic factors now known to exist in speech generation. We propose and implement here a signal model based more directly upon the phsyics of of speech generation. We also implement parametric control of the synthesis model by an adaptive procedure that minimizes the spectral difference between a human speech input and the synthetic output of the model.The adapted parameters constitute a low bit-rate representation of the input human speech. We test a preliminary form of the system by computer simulation and demonstrate that in simple inital trials the signal model is able to adapt in a realistic manner.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
42
Average
Top 10%
Average
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