16 citations · 71 across the 32 of their papers we have counts for
5 papers · 2 filters
Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs
Chandrakant Bothe, Sven Magg, Cornelius Weber +1
Spoken language understanding is one of the key factors in a dialogue system, and a context in a conversation plays an important role to understand the current utterance. In this w…
Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks
Chandrakant Bothe, Sven Magg, Cornelius Weber +1
Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one. It was shown that the performance impr…
A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
Chandrakant Bothe, Cornelius Weber, Sven Magg +1
Dialogue act recognition is an important part of natural language understanding. We investigate the way dialogue act corpora are annotated and the learning approaches used so far.…
Reusing Neural Speech Representations for Auditory Emotion Recognition
Egor Lakomkin, Cornelius Weber, Sven Magg +1
Acoustic emotion recognition aims to categorize the affective state of the speaker and is still a difficult task for machine learning models. The difficulties come from the scarcit…
Automatically augmenting an emotion dataset improves classification using audio
Egor Lakomkin, Cornelius Weber, Stefan Wermter
In this work, we tackle a problem of speech emotion classification. One of the issues in the area of affective computation is that the amount of annotated data is very limited. On…