Vowel Classification by Global Dynamic Modeling
Xiaolin Liu, Richard J. Povinelli, Michael T. Johnson · 2003
A novel approach is presented in this paper for vowel classification by analyzing the dynamics of speech production in a reconstructed phase space. The proposed approach has the advantage of capturing nonlinear dynamics that exist in speech production. Global flow reconstruction is used to generate a compact and quantitative description of the structure and trajectory of vowel attractors in a reconstructed phase space. A distance measure is defined to quantify the dynamic similarity between phoneme attractors. Templates of the dynamics for each vowel class are selected by cluster analysis. Classifying out-of-sample vowel phonemes is done using a nearest neighbor classifier. Experiments are conducted on both speaker dependent and independent vowel classification tasks using the TIMIT corpus. The preliminary experimental results show that vowel classification by nonlinear dynamics analysis can produce very similar result when compared with a classifier using Mel frequency cepstral coefficient (MFCC) features. 1.