Grouping Miller–Nicely by linear vector space rotations

Abstract

Human speech recognition has been studied using response to CV speech stimuli. Miller and Nicely (1955) studied such data in the form of confusion matrices to obtain insight into the psychological structure of the phone in noise. Here, the confusion matrices are modeled as phone coordinates in a high dimensional perceptual vector space. The model generalizes to an eigenvalue decomposition (EVD) [Allen (2004)]. This is followed by agglomerative hierarchical clustering of the transformed data, and an automated process is used to identify the main clusters. The resulting EVD clustering is very similar to other Miller–Nicely groupings, based on both production and MDS derived features, but is more model based. It was found that there is a gradual and highly consistent change in the clustering of sounds, independent of cluster size and configuration. By examining the change in similarity between various speech sounds, it is hoped that perceptual features may be uniquely identified.