@inproceedings{f61b45a50dba467991dddb3c8f3d509e,
title = "Discovering dimensions of perceived vocal expression in semi-structured, unscripted oral history accounts",
abstract = "What do people hear in expressive, unprompted speech? And how can their descriptions be transformed into a representative set of dimensions of vocal expression? This paper presents a methodology for collecting user description of vocal expression, transforms the user descriptions into a set of measurable expressive dimensions, and derives a representative feature set and baseline classifiers across these dimensions. The resulting classifiers recognized the top 13 dimensions over an oral history corpus, with a maximum unweighted recall score of 80.5%",
keywords = "Perception, acoustic correlates, oral histories, paralingual speech, unscripted speech, vocal expression",
author = "Mary Pietrowicz and Mark Hasegawa-Johnson and Karrie Karahalios",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7953247",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5695--5699",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
address = "United States",
}