Oh, Jeez! or Uh-huh? A Listener-aware Backchannel Predictor on ASR Transcriptions
This paper presents our latest investigation on modeling backchannel in conversations. Motivated by a proactive backchanneling theory, we aim at developing a system which acts as a proactive listener by inserting backchannels, such as continuers and assessment, to influence speakers. Our model takes into account not only lexical and acoustic cues, but also introduces the simple and novel idea of using listener embeddings to mimic different backchanneling behaviours. Our experimental results on the Switchboard benchmark dataset reveal that acoustic cues are more important than lexical cues in this task and their combination with listener embeddings works best on both, manual transcriptions and automatically generated transcriptions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning to Generate Context-Sensitive Backchannel Smiles for Embodied AI Agents with Applications in Mental Health Dialogues
Addressing the critical shortage of mental health resources for effective screening, diagnosis, and treatment remains a significant challenge. This scarcity underscores the need for innovative solutions, particularly in …
Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children
Social robots and interactive computer applications have the potential to foster early language development in young children by acting as peer learning companions. However, studies have found that children only trust ro…
Pupil DilationTime SeriesTime Series AnalysisTime Series ClassificationModeling Speaker-Listener Interaction for Backchannel Prediction
We present our latest findings on backchannel modeling novelly motivated by the canonical use of the minimal responses Yeah and Uh-huh in English and their correspondent tokens in German, and the effect of encoding the s…
PredictionExploring Semi-Supervised Learning for Predicting Listener Backchannels
Developing human-like conversational agents is a prime area in HCI research and subsumes many tasks. Predicting listener backchannels is one such actively-researched task. While many studies have used different approache…
Japanese conversation corpus for training and evaluation of backchannel prediction model.
In this paper, we propose an experimental method for building a specialized corpus for training and evaluating backchannel prediction models of spoken dialogue. To develop a backchannel prediction model using a machine l…
Spoken Dialogue Systems