CONF Cernak_INTERSPEECH_2016/IDIAP Sound Pattern Matching for Automatic Prosodic Event Detection Cernak, Milos Asaei, Afsaneh Honnet, Pierre-Edouard Garner, Philip N. Bourlard, Hervé EXTERNAL https://publications.idiap.ch/attachments/papers/2016/Cernak_INTERSPEECH_2016.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Cernak_Idiap-RR-03-2016 Related documents Interspeech San Francisco, USA 2016 REPORT Cernak_Idiap-RR-03-2016/IDIAP Sound Pattern Matching for Automatic Prosodic Event Detection Cernak, Milos Asaei, Afsaneh Honnet, Pierre-Edouard Garner, Philip N. Bourlard, Hervé Automatic prosodic event detection nearest neighbour rule of classification. phonological posteriors word emphasis EXTERNAL https://publications.idiap.ch/attachments/reports/2016/Cernak_Idiap-RR-03-2016.pdf PUBLIC Idiap-RR-03-2016 2016 Idiap March 2016 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 features 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.