Search Results for author:
Found 7 papers, 1 papers with code
Oh, Jeez! or Uh-huh? A Listener-aware Backchannel Predictor on ASR Transcriptions
This paper presents our latest investigation on modeling backchannel in conversations.
Modeling 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 speaker-listener interaction.
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
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. emotion recognition, engagement level prediction and backchanneling) conversational agents.
ADVISER: A Dialog System Framework for Education \& Research
In this paper, we present ADVISER - an open source dialog system framework for education and research purposes.
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions
This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Lexico-acoustic Neural-based Models for Dialog Act Classification
We propose a neural model that processes both lexical and acoustic features for classification.
Neural-based Context Representation Learning for Dialog Act Classification
We explore context representation learning methods in neural-based models for dialog act classification.
