@InProceedings{Kraljevski2015_358,
author = {Ivan Kraljevski and Diane Hirschfeld},
booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2015},
title = {Language Model Adaptation for Transcription of Banking Protocols},
year = {2015},
editor = {Günther Wirsching},
month = mar,
pages = {81--88},
publisher = {TUDpress, Dresden},
abstract = {This paper presents an approach for adaptation of a LVCSR system on a
specific domain - speech transcriptions for automated protocol generation during
investment consultations. Because of the small amount of available domain-specific
speech and textual data, it is not possible to create reliable statistical language model, therefore, word categories containing synonyms were used to train a word-class
based model. To provide an appropriate domain-specific textual corpus for language
model training, data augmentation was employed by creation of grammar rules and
generation of large number of “artificial” sentences. Such language model could be
used as standalone or could be merged with the general model. Recognition
performance was compared across different language models: the domain-specific
model, the general purpose model and as well as their weighted combinations. The
results justified the proposed approach for domain-specific language modeling on
banking protocols transcriptions.},
isbn = {978-3-959080-00-2},
issn = {0940-6832},
keywords = {Spracherkennung},
url = {https://www.essv.de/pdf/2015_81_88.pdf},
}