1 citations · 3 across the 7 of their papers we have counts for
8 papers
Oh, Jeez! or Uh-huh? A Listener-aware Backchannel Predictor on ASR Transcriptions
Daniel Ortega, Chia-Yu Li, Ngoc Thang Vu
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…
Modeling Speaker-Listener Interaction for Backchannel Prediction
Daniel Ortega, Sarina Meyer, Antje Schweitzer +1
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…
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
Chia-Yu Li, Daniel Ortega, Dirk Väth +9
We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e.g.…
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions
Daniel Ortega, Chia-Yu Li, Gisela Vallejo +2
This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions. We propose a novel approach that combines convolutional n…
Lexico-acoustic Neural-based Models for Dialog Act Classification
Daniel Ortega, Ngoc Thang Vu
Recent works have proposed neural models for dialog act classification in spoken dialogs. However, they have not explored the role and the usefulness of acoustic information. We pr…
Neural-based Context Representation Learning for Dialog Act Classification
Daniel Ortega, Ngoc Thang Vu
We explore context representation learning methods in neural-based models for dialog act classification. We propose and compare extensively different methods which combine recurren…