Abstract

A long standing view in speech production research posits that articulatory representations are low dimensional. Conceptual and computational models have been built based on this view. In this work we explore the nature of low dimensional representations derived directly from articulatory signals based on sparsity constraints. Specifically, we present a method to examine how well derived representations of “primitive movements” of speech articulation can be used to classify broad phone categories. We first extract these spatiotemporal primitives from a data matrix of human speech articulation data using a weakly-supervised learning method that attempts to find a part-based representation of the data in terms of basis units (or primitives) and their corresponding activations over time. For each phone interval, we then derive a feature representation that captures the co-occurrences between the activations of the …

Date

2013

Authors

Vikram Ramanarayanan, Maarten Van Segbroeck, Shrikanth S Narayanan

Journal

Proc. Workshop on Speech Production in Automatic Speech Recognition