@article {475,
	title = {Automatic Analysis of Speech Prosody in Dutch},
	year = {2020},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {In this talk we present the first publicly available tool for automatic analysis of speech prosody (AASP) in Dutch. Incorporating the state-of-the-art analytical frameworks, AASP enables users to analyze prosody from two different theoretical perspectives. Structurally, AASP analyzes prosody in terms of prosodic events within the auto-segmental metrical framework, hypothesizing prosodic labels in accordance with Transcription of Dutch Intonation (ToDI). Holistically, by means of the Functional Principal Component Analysis (FPCA) AASP generates mathematical functions that capture changes in the shape of a pitch contour. Regarding ToDI, AASP performs four tasks including pitch accent detection, pitch accent classification, prosodic boundary detection, and prosodic boundary tone classification. Using SVM, AASP performs with accuracy comparable to similar tools for other languages for pitch accent detection, prosodic boundary detection, and prosodic boundary tone classification. Notably, we have found that by combining functional features extracted from FPCA with conventional acoustic features, AASP can attain a higher accuracy for pitch accent classification (76.87\%) than AuToBI for English using conventional acoustic features (71.6\%). Regarding FPCA, AASP outputs the weights of principal components that capture core variations in the shape of pitch contours in a .csv file, which can be directly used for further statistical analysis.
Published as a Docker container, AASP can be set up on various operating systems in only two steps. Moreover, the tool is accessed through a graphic user interface, making it accessible to users with limited programming skills. It has also the potential to be adapted for prosodic analysis in other languages.
},
	author = {Hu, Na and Janssen, Berit and Hanssen, Judith and Carlos Gussenhoven and Aoju Chen}
}
@article {455,
	title = {Prosody differs between objective and subjective causal relations in English},
	year = {2019},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {Amsterdam, The Netherlands},
	abstract = {Causality can be established either objectively or subjectively. The causality expressed in example (1) is objective, because both the consequence and the cause are real events; while the causality in example (2) is subjective, because {\textquotedblleft}Heidi is talented{\textquotedblright} is one{\textquoteright}s opinion. In languages like Dutch, these two types of causality are expressed by specialized connectives (omdat/want). However, in English, they are both expressed by because. Hence, the types of causality are left unspecified on the lexical level (if other lexical cues are not present). This leads to the question of whether these two types of causality are distinguished by non-lexical cues, e.g. prosody. We investigated this question in both forward and backward causals using a dialogue task. Taking the Bayesian approach, we examined a wide range of prosodic features, including not only static measures such as pitch and duration, but also dynamic measures on the shape of pitch contours extracted by Functional Principal Component Analysis. The results showed that in comparison with objective causals, subjective causals were produced with higher F0 maximum, lower F0 minimum, longer duration, and also with distinctive contour shapes. These results indicate a trade-off between lexical and prosodic cues.

(1a) Heidi is thrilled because she won the first prize at the art festival. [backward]
(1b) Heidi won the first prize at the art festival so she is thrilled. [forward]
(2a) Heidi is talented because she won the first prize at the art festival. [backward]
(2b) Heidi won the first prize at the art festival so she is talented. [forward]

},
	author = {Hu, Na and Aoju Chen and Hugo Quen{\'e} and Ted Sanders}
}
