Neural Networks for Text-to-Speech Phoneme Recognition

Abstract This paper presents two different artificial neural network approaches for phoneme recognition for text-to-speech applications: Staged Backpropagation Neural Networks and SelfOrganizing Maps. Several current commercial approaches rely on an exhaustive dictionary approach for text-to-phoneme conversion. Applying neural networks for phoneme mapping for text-to-speech conversion creates a fast distributed recognition engine. This engine not only supports the mapping of missing words on the database, but it can also mitigate contradictions related to different pronunciations for the same word. The ANNs presented in this work were trained based on the 2000 most common words in American English. Performance metrics for the 5000, 7000 and 10000 most common words in English were also estimated to test the robustness of these neural networks

Scope for Credit Risk Diversification

This paper considers a simple model of credit risk and derives the limit distribution of losses under different assumptions regarding the structure of systematic risk and the nature of exposure or firm heterogeneity. We derive fat-tailed correlated loss distributions arising from Gaussian risk factors and explore the potential for risk diversification. Where possible the results are generalised to non-Gaussian distributions. The theoretical results indicate that if the firm parameters are heterogeneous but come from a common distribution, for sufficiently large portfolios there is no scope for further risk reduction through active portfolio management. However, if the firm parameters come from different distributions, then further risk reduction is possible by changing the portfolio weights. In either case, neglecting parameter heterogeneity can lead to underestimation of expected losses. But, once expected losses are controlled for, neglecting parameter heterogeneity can lead to overestimation of risk, whether measured by unexpected loss or value-at-risk

Multi-objective optimization of spectra using genetic algorithms

This paper applies genetic algorithms (GAs), a powerful general-purpose biologically motivated optimization technique, to the multi-objective problem of spectrum optimization. Two objectives, color and efficiency, are address using real spectra, although the addition of other objectives (e.g., color rendering, color temperature) is relatively straightforward. The direct application of the method presented is to transform the spectrum of newly developed lighting technologies to have desirable color properties while maximizing efficiency. Other applications of this methodology include the design of a filter for the input of a fiber optic system such that the color at then end of a given length of fiber has particular properties (e.g., appears “white”), while the efficiency of the system is minimally affected. The principal findings described in this paper are the implementation of an efficient multi-objective fitness function tailored to this problem and a method for speeding convergence of the GA by "smoothing the chromosomes." An algorithm, data and results from several approaches are presented

Fuzzy roc curves for unsupervised nonparametric ensemble techniques

Abstract — This paper explores a novel ensemble technique for unsupervised classification using nonparametric statistics. Multiple classification systems (MCS), or ensemble techniques, involve considering several classification methods or multiple outputs from the same method and devising techniques to reach a decision. The performance of a binary classification system can be measured on a receiver operating characteristic (ROC) curve, and the area under the curve (AUC) is exactly the Wilcoxon Rank Sum or Mann-Whitney U statistic, both of which are nonparametric statistics based upon ranked data. Successful performance of an unsupervised ensemble can be measured through the AUC, and the performance of different aggregation techniques for the combination of the multiple classification system decision values, or rankings in this paper, is illustrated. Aggregation techniques are based upon fuzzy logic theory, creating the fuzzy ROC curve. The one-class SVM is utilized for the unsupervised classification. I

ABSTRACT PHONEME RECOGNITION WITH STAGED NEURAL NETWORKS

This paper presents a staged series of artificial neural networks (ANNs) for phoneme recognition for text-to-speech applications. Contrary from much of the prior published literature this approach is not restricted to monosyllabic words or the pronunciation of single multi-syllabic words, but can readily be embodied in a program that allows for the reading of a complete text. Also, it does not require pre-processing to align the letters and phonemes on the training data. The training data utilized are the 2000 most common words in American English. As an illustration it is shown that the staged neural neural network approach works excellent for a sample text (in this case the first paragraph of Frank Baum’s “The Wonderful Wizard of Oz”). I