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This paper describes our exploratory work in applying the Automatic ToBI annotation system (AuToBI), originally developed for Standard American English, to European Portuguese. This work is motivated by the current availability of large amounts of (highly spontaneous) transcribed data and the need to further enrich those transcripts with prosodic information. Manual prosodic annotation, however, is almost impractical for extensive data sets. For that reason, automatic systems such as Au-ToBi stand as an alternate solution. We have started by applying the AuToBI prosodic event detection system using the existing English models to the prediction of prominent prosodic events (accents) in European Portuguese. This approach achieved an overall accuracy of 74% for prominence detection, similar to state-of-the-art results for other languages. Later, we have trained new models using prepared and spontaneous Portuguese data, achieving a considerable improvement of about 6% accuracy (absolute) over the existing English models. The achieved results are quite encouraging and provide a starting point for automatically predicting prominent events in European Portuguese.

Abstract. This paper performs a global analysis of entrainment between dyads in map-task dialogues in European Portuguese (EP), including 48 dialogues, between 24 speakers. Our main goals focus on the acoustic-prosodic similarities between speakers, namely if there are global entrainment cues displayed in the dialogues, if there are degrees of entrainment manifested in distinct sets of features shared amongst the speakers, if entrainment depends on the role of the speaker as either giver or follower, and also if speakers tend to entrain more with specific pairs regardless of the role. Results show global entrainment in almost all the dyads, but the degrees of entrainment (stronger within the same gender), and the role effects tend to be less striking than the interlocutors' effect. Globally, speakers tend to be more similar to their own speech in other dialogues than to their partners. However, speakers are also more similar to their interlocutors than to speakers with whom they never spoke.

Abstract. This paper investigates the use of heterogeneous speech corpora for automatic assessment of personality traits in terms of the BigFive OCEAN dimensions. The motivation for this work is twofold: the need to develop methods to overcome the lack of children's speech corpora, particularly severe when targeting personality traits, and the interest on cross-age comparisons of acoustic-prosodic features to build robust paralinguistic detectors. For this purpose, we devise an experimental setup with age mismatch utilizing the Interspeech 2012 Personality Subchallenge, containing adult speech, as training data. As test data, we use a corpus of children's European Portuguese speech. We investigate various features sets such as the Sub-challenge baseline features, the recently introduced eGeMAPS features and our own knowledge-based features. The preliminary results bring insights into cross-age and -language detection of personality traits in spontaneous speech, pointing out to a stable set of acoustic-prosodic features for Extraversion and Agreeableness in both adult and child speech.

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