Sound Pattern Matching for Automatic Prosodic Event Detection

Prosody in speech is manifested by variations of loudness, exaggeration of pitch, and specific phonetic variations of prosodic segments. For example, in the stressed and unstressed syllables, there are differences in place or manner of articulation, vowels in unstressed syllables may have a more central articulation, and vowel reduction may occur when a vowel changes from a stressed to an unstressed position. In this paper, we characterize the sound patterns using phonological posteriors to capture the phonetic variations in a concise manner. The phonological posteriors quantify the posterior probabilities of the phonological classes given the input speech acoustics, and they are obtained using the deep neural network (DNN) computational method. Built on the assumption that there are unique sound patterns in different prosodic segments, we devise a sound pattern matching (SPM) method based on 1-nearest neighbour classifier. In this work, we focus on automatic detection of prosodic stress placed on words, called also emphasized words. We evaluate the SPM method on English and French data with emphasized words. The word emphasis detection works very well also on cross-lingual tests, that is using a French classifier on English data, and vice versa.