Ludusan B, Schröer M, Rossi M, Wagner P. The co-use of laughter and head gestures across speech styles. In: Interspeech 2023. Proceedings. ISCA; 2023: 3592-3596.

ISSN: 2958-1796

Entrainment is a phenomenon that occurs across several modalities and at different linguistic levels in conversation.Previous work has shown that its effects may be modulated by conversation extrinsic factors, such as the relation between the interlocutors or the speakers' traits.The current study investigates the role of conversation type on laughter entrainment.Employing dyadic interaction materials in German, containing two conversation types (free dialogues and task-based interactions), we analyzed three measures of entrainment previously proposed in the literature.The results show that the entrainment effects depend on the type of conversation, with two of the investigated measures being affected by this factor.These findings represent further evidence towards the role of situational aspects as a mediating factor in conversation.

Ludusan B, Schröer M, Wagner P. Investigating phonetic convergence of laughter in conversation. In: Interspeech 2022. Proceedings. ISCA: ISCA; 2022: 1332-1336.

A number of cues, both linguistic and nonlinguistic, have been found to mark discourse structure in conversation.This paper investigates the role of laughter, one of the most encountered non-verbal vocalizations in human communication, in the signalling of turn boundaries.We employ a corpus of informal dyadic conversations to determine the likelihood of laughter at the end of speaker turns and to establish the potential role of laughter in discourse organization.Our results show that, on average, about 10% of the turns are marked by laughter, but also that the marking is subject to individual variation, as well as effects of other factors, such as the type of relationship between speakers.More importantly, we find that turn ends are twice more likely than transition relevance places to be marked by laughter, suggesting that, indeed, laughter plays a role in marking discourse structure.

It has been established across a wide range of communicative behaviours that conversational partners tend to become more similar during their interaction. This phenomenon, often called entrainment, has been shown to take place not only at various linguistic levels, but also across different modalities. We investigated in this study whether entrainment can be found in the use of paralinguistic phenomena in conversation. Laughter is a vocalization widely recognized across cultures, and one of the most encountered paralinguistic events in spontaneous interactions. Using conversational data from three distinct languages: French, German and Mandarin Chinese, we examined two facets of entrainment: temporal and form-related. Five entrainment measures, computed across two different levels of linguistic organization, were considered in our analysis. Support was found for temporal entrainment at the laughter-token level, in how speakers of a dialogue distribute their laughter events throughout the conversation. At the turn level, speakers of all three languages showed evidence for entrainment, by aligning their laughter more with the beginning and the end of their turns. Moreover, this phenomenon seemed to be enhanced in the second half of the examined recordings, compared to the first half. The study found support also for form-related entrainment, with conversational partners employing more similar intensity levels for consecutive, than for non-consecutive laughter. Furthermore, we show that the entrainment aspects captured by our measures are independent of the degree of familiarity between the speakers.

Ludusan B, Wagner P, Włodarczak M. Cue interaction in the perception of prosodic prominence: the role of voice quality. In: Interspeech 2021. Proceedings. ISCA; 2021: 1006-1010.

Ludusan B, Wagner P. An Evaluation of Manual and Semi-Automatic Laughter Annotation. In: Proceedings of Interspeech 2020. ISCA; 2020: 621-625.

Ludusan B, Jorschick A, Mazuka R. Nasal Consonant Discrimination in Infant- and Adult-Directed Speech. In: Interspeech 2019. Proc. Interspeech 2019. ISCA: ISCA; 2019: 3584-3588.

Ludusan B, Wagner P. Laughter Dynamics in Dyadic Conversations. In: Proceedings of Interspeech. 2019.

It is well known that prosodic information is used by infants in early language acquisition. In particular, prosodic boundaries have been shown to help infants with sentence and wordlevel segmentation. In this study, we extend an unsupervised method for word segmentation to include information about prosodic boundaries. The boundary information used was either derived from oracle data (handannotated), or extracted automatically with a system that employs only acoustic cues for boundary detection. The approach was tested on two different languages, English and Japanese, and the results show that boundary information helps word segmentation in both cases. The performance gain obtained for two typologically distinct languages shows the robustness of prosodic information for word segmentation. Furthermore, the improvements are not limited to the use of oracle information, similar performances being obtained also with automatically extracted boundaries.

In this paper we propose a novel method for integrating stress information in the decoding step of a speech recognizer.A multiscale rhythm model was used to determine the stress scores for each syllable, which are further used to reinforce paths during search.Two strategies for integrating the stress were employed: the first one reinforces paths through all the syllables with a value proportional to the their stress score, while the second one enhances paths passing only through stressed syllables, but with a constant value.The former strategy slightly outperforms the later, bringing a relative improvement of more than 2% over the baseline.Furthermore, the stress information proved to be a robust feature, by performing well even for foreign-accented speech.

This work presents a novel framework to guide the Viterbi decoding process of a hidden Markov model based speech recognition system by means of broad phonetic classes. In a first step, decision trees are employed, along with frame and segment based attributes, in order to detect broad phonetic classes in the speech signal. Then, the detected phonetic classes are used to reinforce paths in the search process, either at every frame or at phonetically significant landmarks. Results obtained on French broadcast news data show a relative improvement in word error rate of about 2% with respect to the baseline.

In this work, we present a new approach for the classification and detection of speech units for the use in landmark or event-based speech recognition systems. We use segmentation to model any time-variable speech unit by a fixed-dimensional observation vector, in order to train a committee of boosted decision stumps on labeled training data. Given an unknown speech signal, the presence of a desired speech unit is estimated by searching for each time frame the corresponding segment, that provides the maximum classification score. This approach improves the accuracy of a phoneme classification task by 1.7%, compared to classification using HMMs. Applying this approach to the detection of broad phonetic landmarks inside a landmark-driven HMM-based speech recognizer significantly improves speech recognition.