Papers by Sven Magg
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators (2020.lrec-1)
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| Challenge: | Emotion recognition helps to build natural dialogue systems. |
| Approach: | They propose to use a recurrent neural model to annotate emotion corpora with dialogue act labels and an ensemble annotator to extract the final dialogue act label. |
| Outcome: | The proposed model annotates two accessible multi-modal emotion corpora with and without context and extracts the final dialogue act label. |
A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks (L18-1)
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| Challenge: | Existing models of dialogue act classification work on the utterance-level and only very few consider context. |
| Approach: | They propose to use a character-level language model to classify dialogue acts without context . they find that the preceding utterances are a context of the current utterant . |
| Outcome: | The proposed method improves on the Switchboard Dialogue Act corpus . it includes context and leads to 3% higher accuracy . |
KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos (D18-2)
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| Challenge: | KT-Speech-Crawler is an automated dataset building tool for speech recognition. |
| Approach: | They propose an approach for automatic dataset construction for speech recognition by crawling YouTube videos. |
| Outcome: | The proposed algorithm can obtain 150 hours of transcribed speech in a day with an estimated 3.5% word error rate. |