Date of Original Version

12-2013

Type

Conference Proceeding

Journal Title

Proceedings of IEEE Workshop on Automatic Speech Recognition (ASRU)

First Page

19

Last Page

24

Rights Management

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Abstract or Description

Out-of-vocabulary (OOV) words can appear more than once in a conversation or over a period of time. Such multiple instances of the same OOV word provide valuable information for learning the lexical properties of the word. Therefore, we investigated how to estimate better pronunciation, spelling and part-of-speech (POS) label for recurrent OOV words. We first identified recurrent OOV words from the output of a hybrid decoder by applying a bottom-up clustering approach. Then, multiple instances of the same OOV word were used simultaneously to learn properties of the OOV word. The experimental results showed that the bottom-up clustering approach is very effective at detecting the recurrence of OOV words. Furthermore, by using evidence from multiple instances of the same word, the pronunciation accuracy, recovery rate and POS label accuracy of recurrent OOV words can be substantially improved.

DOI

10.1109/ASRU.2013.6707699

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Published In

Proceedings of IEEE Workshop on Automatic Speech Recognition (ASRU), 19-24.