Interaction Quality as a Human-Human Task-Oriented Conversation Performance

Spirina, Anastasiia; Vaskovskaia, Olesia; Sidorov, Maxim; Schmitt, Alexander

doi:10.1007/978-3-319-43958-7_48

Cited by 4 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, our system is supposed to distinguish between childand adult-directed utterances. A possible application for such a system of adult-child conversation monitoring is the estimation of children's and adults' conversational behaviour that will allow us to measure Interaction Quality (IQ) (Spirina et al, 2016). According to this complex metric, we will be able to assess the children's progress in maintaining conversations.…”

Section: Introductionmentioning

confidence: 99%

Cross-Corpus Data Augmentation for Acoustic Addressee Detection

Akhtiamov

Siegert

Karpov

et al. 2019

Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

View full text Add to dashboard Cite

Acoustic addressee detection (AD) is a modern paralinguistic and dialogue challenge that especially arises in voice assistants. In the present study, we distinguish addressees in two settings (a conversation between several people and a spoken dialogue system, and a conversation between several adults and a child) and introduce the first competitive baseline (unweighted average recall equals 0.891) for the Voice Assistant Conversation Corpus that models the first setting. We jointly solve both classification problems, using three models: a linear support vector machine dealing with acoustic functionals and two neural networks utilising raw waveforms alongside with acoustic low-level descriptors. We investigate how different corpora influence each other, applying the mixup approach to data augmentation. We also study the influence of various acoustic context lengths on AD. Two-second speech fragments turn out to be sufficient for reliable AD. Mixup is shown to be beneficial for merging acoustic data (extracted features but not raw waveforms) from different domains that allows us to reach a higher classification performance on human-machine AD and also for training a multipurpose neural network that is capable of solving both human-machine and adult-child AD problems.

show abstract

Section: Introductionmentioning

confidence: 99%