Combining semantic word classes and sub-word unit speech recognition for robust OOV detection

Horndasch A, Batliner A, Kaufhold C, Nöth E (2016)

Publication Language: English

Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Publication year: 2016

Publisher: International Speech and Communication Association

Pages Range: 1335-1339

Conference Proceedings Title: 17th Annual Conference of the International Speech Communication Association (INTERSPEECH 2016): Understanding Speech Processing in Humans and Machines

Event location: San Francisco, CA, USA US

ISBN: 978-1-5108-3313-5

URI: https://pdfs.semanticscholar.org/70f1/4384711a26d2f38c855da729c03c8066bf16.pdf

DOI: 10.21437/Interspeech.2016-1250

Abstract

Out-of-vocabulary words (OOVs) are often the main reason for the failure of tasks like automated voice searches or human-machine dialogs. This is especially true if rare but task-relevant content words, e.g. person or location names, are not in the recognizer's vocabulary. Since applications like spoken dialog systems use the result of the speech recognizer to extract a semantic representation of a user utterance, the detection of OOVs as well as their (semantic) word class can support to manage a dialog successfully. In this paper we suggest to combine two well-known approaches in the context of OOV detection: semantic word classes and OOV models based on sub-word units. With our system, which builds upon the widely used Kaldi speech recognition toolkit, we show on two different data sets that - compared to other methods - such a combination improves OOV detection performance for open word classes at a given false alarm rate. Another result of our approach is a reduction of the word error rate (WER).

Authors with CRIS profile

Axel Horndasch Professur für Informatik (Mustererkennung) Anton Batliner Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Caroline Kaufhold Professur für Informatik (Mustererkennung) Elmar Nöth Professur für Informatik (Mustererkennung)

How to cite

APA:

Horndasch, A., Batliner, A., Kaufhold, C., & Nöth, E. (2016). Combining semantic word classes and sub-word unit speech recognition for robust OOV detection. In 17th Annual Conference of the International Speech Communication Association (INTERSPEECH 2016): Understanding Speech Processing in Humans and Machines (pp. 1335-1339). San Francisco, CA, USA, US: International Speech and Communication Association.

MLA:

Horndasch, Axel, et al. "Combining semantic word classes and sub-word unit speech recognition for robust OOV detection." Proceedings of the 17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016, San Francisco, CA, USA International Speech and Communication Association, 2016. 1335-1339.

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