@InProceedings{Stehwien2017_239,
author = {Sabrina Stehwien and Ngoc Thang Vu},
booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2017},
title = {First step Towards Enhancing Word Embeddings with Pitch Accent Features for DNN-based Slot Filling on Recognized Text},
year = {2017},
editor = {Jürgen Trouvain and Ingmar Steiner and Bernd Möbius},
month = mar,
pages = {194--201},
publisher = {TUDpress, Dresden},
abstract = {Slot filling, as a subtask of spoken language understanding, is designed
to extract key query terms from text after it has been recognized from speech. Most
state-of-the-art models do not, however, take recognition error into account and
show a substantial drop in performance when applied to recognized text. One
source of information that marks important parts of utterances and is available
from speech data is prosody. Since pitch accents have been shown to correlate
with semantic slots in the ATIS benchmark corpus, we combine these as features
with word embeddings for slot filling on ATIS and compare their impact on the
performance of two state-of-the-art models when applied to recognized text. Our
experimental results and analysis show that extending word embeddings with pitch
accent features slightly improves slot filling systems on recognized text.},
isbn = {978-3-959080-92-7},
issn = {0940-6832},
keywords = {Sprachmodellierung},
url = {https://www.essv.de/pdf/2017_194_201.pdf},
}