| Presentation # | 6 |
| Session: | Spoken Language Understanding |
| Session Time: | Wednesday, December 19, 10:00 - 12:00 |
| Presentation Time: | Wednesday, December 19, 10:00 - 12:00 |
| Presentation: |
Poster
|
| Topic: |
Spoken language understanding: |
| Paper Title: |
End-to-end named entity and semantic concept extraction from speech |
| Authors: |
Sahar Ghannay; University of Le Mans | | |
| | Antoine Caubrière; University of Le Mans | | |
| | Yannick Estève; University of Le Mans | | |
| | Nathalie Camelin; University of Le Mans | | |
| | Edwin Simonnet; University of Le Mans | | |
| | Antoine Laurent; University of Le Mans | | |
| | Emmanuel Morin; University of Nantes | | |
| Abstract: |
Named entity recognition (NER) is among SLU tasks that usually extract semantic information from textual documents. Usually, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs. Such approach has some disadvantages (error propagation, sub-optimal tuning of ASR systems in regards to the final task, reduced space search at the ASR output level,...) and it is known that more integrated approaches outperform sequential ones, when they can be applied. In this paper, we explore an end-to-end approach that directly extracts named entities from speech, though a unique neural architecture. On a such way, a joint optimization is possible for both ASR and NER. Experiments are carried on French data easily accessible, composed of data distributed in several evaluation campaigns. The results are promising since this end-to-end approach provides similar results (F-measure=0.66 on test data) than a classical pipeline approach to detect named entity categories (F-measure=0.64). Last, we also explore this approach applied to semantic concept extraction, through a slot filling task known as a spoken language understanding problem, and also observe an improvement in comparison to a pipeline approach. |