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
ISBN: 978-1-5108-3313-5
URI: https://pdfs.semanticscholar.org/70f1/4384711a26d2f38c855da729c03c8066bf16.pdf
DOI: 10.21437/Interspeech.2016-1250
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).
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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