Time-frequency shift-tolerance and counterpropagation network with applications to phoneme recognition

Human speech signals are inherently multi-component non-stationary signals. Recognition schemes for classification of non-stationary signals generally require some kind of temporal alignment to be performed. Examples of techniques used for temporal alignment include hidden Markov models and dynamic time warping. Attempts to incorporate temporal alignment into artificial neural networks have resulted in the construction of time-delay neural networks. The nonstationary nature of speech requires a signal representation that is dependent on time. Time-frequency signal analysis is an extension of conventional time-domain and frequency-domain analysis methods. Researchers have reported on the effectiveness of time-frequency representations to reveal the time-varying nature of speech. In this thesis, a recognition scheme is developed for temporal-spectral alignment of nonstationary signals by performing preprocessing on the time-frequency distributions of the speech phonemes. The resulting representation is independent of any amount of time-frequency shift and is time-frequency shift-tolerant (TFST). The proposed scheme does not require time alignment of the signals and has the additional merit of providing spectral alignment, which may have importance in recognition of speech from different speakers. A modification to the counterpropagation network is proposed that is suitable for phoneme recognition. The modified network maintains the simplicity and competitive mechanism of the counterpropagation network and has additional benefits of fast learning and good modelling accuracy. The temporal-spectral alignment recognition scheme and modified counterpropagation network are applied to the recognition task of speech phonemes. Simulations show that the proposed scheme has potential in the classification of speech phonemes which have not been aligned in time. To facilitate the research, an environment to perform time-frequency signal analysis and recognition using artificial neural networks was developed. The environment provides tools for time-frequency signal analysis and simulations of of the counterpropagation network

An Analysis of Minimum Entropy Time-Frequency Distributions

The subject area of time-frequency analysis is concerned with creating meaningful representations of signals in the time-frequency domain that exhibit certain properties. Different applications require different characteristics in the representation. Some of the properties that are often desired include satisfying the time and frequency marginals, positivity, high localization, and strong finite support. Proper time-frequency distributions, which are defined as distributions that are manifestly positive and satisfy both the time and frequency marginals, are of particular interest since they can be viewed as a joint time-frequency density function and ensure strong finite support. Since an infinite number of proper time-frequency distributions exist, it is often necessary to impose additional constraints on the distribution in order to create a meaningful representation of the signal. A significant amount of research has been spent attempting to find constraints that produce meaningful representations.Recently, the idea was proposed of using the concept of minimum entropy to create time-frequency distributions that are highly localized and contain a large number of zero-points. The proposed method starts with an initial distribution that is proper and iteratively reduces the total entropy of the distribution while maintaining the positivity and marginal properties. The result of this method is a highly localized, proper TFD.This thesis will further explore and analyze the proposed minimum entropy algorithm. First, the minimum entropy algorithm and the concepts behind the algorithm will be introduced and discussed. After the introduction, a simple example of the method will be examined to help gain a basic understanding of the algorithm. Next, we will explore different rectangle selection methods which define the order in which the entropy of the distribution is minimized. We will then evaluate the effect of using different initial distributions with the minimum entropy algorithm. Afterwards, the results of the different rectangle selection methods and initial distributions will be analyzed and some more advanced concepts will be explored. Finally, we will draw conclusions and consider the overall effectiveness of the algorithm