Prosody is a central part of human speech, with prosodic modulations of the signal expressing important communicative functions. Yet, the exact mechanisms of how listeners map prosodic aspects of the speech signal onto speaker-intended discourse functions are only poorly understood. Here we present three perception experiments that test the mapping between the prosodic form of a heard utterance and possible information structural categories (here: focus and givenness) determined by a discourse context. Results suggest varying degrees of accuracy dependent on the specific information structure categories that are presented to the listener in the experiment (the target and the competitor). Moreover, listeners are sometimes biased towards or against certain used discourse contexts. These biases are compatible with the idea that listeners infer speaker intentions based not only on bottom-up processing of acoustic cues but also on probabilistic knowledge about how likely prosodic forms co-occur with specific discourse contexts.

Prosody is a central part of human speech, with prosodic modulations of the signal expressing important communicative functions. Yet, the exact mechanisms of how listeners map prosodic aspects of the speech signal onto speaker-intended discourse functions are only poorly understood. Here we present three perception experiments that test the mapping between the prosodic form of a heard utterance and possible information structural categories (here: focus and givenness) determined by a discourse context. Results suggest varying degrees of accuracy dependent on the specific information structure categories that are presented to the listener in the experiment (the target and the competitor). Moreover, listeners are sometimes biased towards or against certain used discourse contexts. These biases are compatible with the idea that listeners infer speaker intentions based not only on bottom-up processing of acoustic cues but also on probabilistic knowledge about how likely prosodic forms co-occur with specific discourse contexts.

When we address speaker states like sleepiness, two partly competing interests can be observed: both within applications and engineering approaches, we aim at utmost performance in terms of classification or regression accuracy-which normally means using a very large feature vector and a brute force approach. The other interest is interpretation: we want to know what tells apart atypical (here: sleepy) speech from typical (here: non-sleepy) speech, i.e., their respective feature characteristics. Both interests cannot be served at the same time. In this paper, we preselect a small number of easily interpretable acoustic-prosodic features modelling spectrum and prosody, based on the literature and on the general idea of sleepiness being characterised by relaxation. Performance obtained with these single features and this small feature vector is compared with the performance obtained with a very large feature vector; moreover, we discuss to which extent the features chosen model relaxation as sleepiness characteristic.

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