Learning acoustic features for English stops with graph-based dimensionality reduction

This study applies a semi-supervised graph-based dimensionality reduction algorithm (Laplacian Eigenmaps [Belkin & Nyogi, 2002]) to analyze burst spectra from adult productions of English /k/ and /t/. Multitaper spectra calculated over 25-ms windows were passed through a gammatone filter bank, which models the auditory periphery’s frequency selectivity and frequency-scale compression. From these psychoacoustic spectra, a graph was constructed: node pairs (two spectra) were connected if they shared a common talker or target word, and connecting edges were weighted by the symmetric Kullback-Leibler divergence between the spectra. This graph’s eigenvectors map the spectra into a low-dimensional feature space. Our preliminary experiments with 512 tokens produced by 16 talkers suggest that this algorithm is able to learn a two-dimensional representation of the bursts which reflects well-established articulatory constriction features. The first dimension linearly separated /k/ from /t/ in the back vowel environ...