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.