In this thesis, novel wavelet techniques are developed to improve parametrization of
speech signals prior to classification. It is shown that non-linear operations carried out
in the wavelet domain improve the performance of a speech classifier and consistently
outperform classical Fourier methods. This is because of the localised nature of the
wavelet, which captures correspondingly well-localised time-frequency features
within the speech signal. Furthermore, by taking advantage of the approximation
ability of wavelets, efficient representation of the non-stationarity inherent in speech
can be achieved in a relatively small number of expansion coefficients. This is an
attractive option when faced with the so-called 'Curse of Dimensionality' problem of
multivariate classifiers such as Linear Discriminant Analysis (LDA) or Artificial
Neural Networks (ANNs). Conventional time-frequency analysis methods such as the
Discrete Fourier Transform either miss irregular signal structures and transients due to
spectral smearing or require a large number of coefficients to represent such
characteristics efficiently. Wavelet theory offers an alternative insight in the
representation of these types of signals.
As an extension to the standard wavelet transform, adaptive libraries of wavelet and
cosine packets are introduced which increase the flexibility of the transform. This
approach is observed to be yet more suitable for the highly variable nature of speech
signals in that it results in a time-frequency sampled grid that is well adapted to
irregularities and transients. They result in a corresponding reduction in the
misclassification rate of the recognition system. However, this is necessarily at the
expense of added computing time.
Finally, a framework based on adaptive time-frequency libraries is developed which
invokes the final classifier to choose the nature of the resolution for a given
classification problem. The classifier then performs dimensionaIity reduction on the
transformed signal by choosing the top few features based on their discriminant power. This approach is compared and contrasted to an existing discriminant wavelet
The overall conclusions of the thesis are that wavelets and their relatives are capable
of extracting useful features for speech classification problems. The use of adaptive
wavelet transforms provides the flexibility within which powerful feature extractors
can be designed for these types of application.
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.