Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition

The presented paper introduces principal component analysis application for dimensionality reduction of variables describing speech signal and applicability of obtained results for the disturbed and fluent speech recognition process. A set of fluent speech signals and three speech disturbances-blocks before words starting with plosives, syllable repetitions, and sound-initial prolongations-was transformed using principal component analysis. The result was a model containing four principal components describing analysed utterances. Distances between standardised original variables and elements of the observation matrix in a new system of coordinates were calculated and then applied in the recognition process. As a classifying algorithm, the multilayer perceptron network was used. Achieved results were compared with outcomes from previous experiments where speech samples were parameterised with the Kohonen network application. The classifying network achieved overall accuracy at 76% (from 50% to 91%, depending on the dysfluency type).

Automated summary

The paper introduces the application of principal component analysis for reducing dimensionality of variables describing speech signal and its applicability in disturbed and fluent speech recognition. The fluent speech signals and three types of speech disturbances were transformed using principal component analysis, and the calculated distances were applied in the recognition process using a multilayer perceptron network. A comparison with previous experiments using the Kohonen network showed an overall accuracy of 76% for the classifying network.