Publication Details
Similarity Scoring for Recognizing Repeated Out-of-VocabularyWords
Kombrink Stefan, Dipl.-Inf -Ling (DCGM FIT BUT)
Karafiát Martin, Ing., Ph.D. (DCGM FIT BUT)
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
out-of-vocabulary, OOV, hybrid word/sub-word recognizer, similarity measure, alignment error model
This paper is on development of a similarity measure to detect repeatedly occuring Out-of-Vocabulary words (OOV), because they carry an important information.
We develop a similarity measure to detect repeatedly occurring Out-of-Vocabulary words (OOV), since these carry important information. Sub-word sequences in the recognition output from a hybrid word/sub-word recognizer are taken as detected OOVs and are aligned to each other with the help of an alignment error model. This model is able to deal with partial OOV detections and tries to reveal more complex word relations such as compound words. We apply the model to a selection of conversational phone calls to retrieve other examples of the same OOV, and to obtain a higher-level description of it such as being a derivation of a known word.
@INPROCEEDINGS{FITPUB9358,
author = "Mirko Hannemann and Stefan Kombrink and Martin Karafi\'{a}t and Luk\'{a}\v{s} Burget",
title = "Similarity Scoring for Recognizing Repeated Out-of-VocabularyWords",
pages = "897--900",
booktitle = "Proceedings of the 11th Annual Conference of the International Speech Communication Association (INTERSPEECH 2010)",
journal = "Proceedings of Interspeech - on-line",
volume = 2010,
number = 9,
year = 2010,
location = "Makuhari, Chiba, JP",
publisher = "International Speech Communication Association",
ISBN = "978-1-61782-123-3",
ISSN = "1990-9772",
language = "english",
url = "https://www.fit.vut.cz/research/publication/9358"
}