Poetic Sound Similarity Vectors Using Phonetic Features

Abstract

A procedure that uses phonetic transcriptions of words to produce a continuous vector-space model of phonetic sound similarity is presented. The vector dimensions of words in the model are calculated using interleaved phonetic feature bigrams, a novel method that captures similarities in sound that are difficult to model with orthographic or phonemic information alone. Measurements of similarity between items in the resulting vector space are shown to perform well on established tests for predicting phonetic similarity. Additionally, a number of applications of vector arithmetic and nearest-neighbor search are presented, demonstrating potential uses of the vector space in experimental poetry and procedural content generation.