Bispectral analysis of speech signals
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Date
06/1997Author
Fackrell, Justin WA
Metadata
Abstract
Techniques which utilise a signal's Higher Order Statistics (HOS) can reveal information
about non-Gaussian signals and nonlinearities which cannot be obtained using
conventional (second-order) techniques. This information may be useful in speech processing
because it may provide clues about how to construct new models of speech
production which are better than existing models.
There has been a recent surge of interest in the application of HOS techniques to speech
processing, but this has been handicapped by a lack of understanding of what the HOS
properties of speech signals are. Without this understanding the HOS information
which is in speech signals can not be efficiently utilised.
This thesis describes an investigation into the use of HOS techniques, in particular the
third-order frequency domain measure called the bispectrum, to speech signals. Several
issues relating to bispectral speech analysis are addressed, including nonlinearity
detection, pitch-synchronous analysis, estimation criteria and stationarity. A flaw is
identified in an existing algorithm for detecting quadratic nonlinearities, and a new
detector is proposed which has better statistical properties. In addition, a new algorithm
is developed for estimating the normalised bispectrum of signals contaminated
by transient noise.
Finally the tools developed in the study are applied to a specially constructed database
of continuant speech sounds. The results are consistent with the hypothesis that speech
signals do not exhibit quadratic nonlinearity.