ISCA Archive Interspeech 2005
ISCA Archive Interspeech 2005

Two-pass strategy for handling OOVs in a large vocabulary recognition task

Odette Scharenborg, Stephanie Seneff

This paper addresses the issue of large-vocabulary recognition in a specific word class. We propose a two-pass strategy in which only major cities are explicitly represented in the first stage lexicon. An unknown word model encoded as a phone loop is used to detect OOV city names (referred to as rare city names). After which SpeM, a tool that can extract words and word-initial cohorts from phone graphs on the basis of a large fallback lexicon, provides an N-best list of promising city names on the basis of the phone sequences generated in the first stage. This N-best list is then inserted into the second stage lexicon for a subsequent recognition pass. Experiments were conducted on a set of spontaneous telephone-quality utterances each containing one rare city name. We tested the size of the N-best list and three types of language models (LMs). The experiments showed that SpeM was able to include nearly 85% of the correct city names into an N-best list of 3000 city names when a unigram LM, which also boosted the unigram scores of a city name in a given state, was used.


doi: 10.21437/Interspeech.2005-545

Cite as: Scharenborg, O., Seneff, S. (2005) Two-pass strategy for handling OOVs in a large vocabulary recognition task. Proc. Interspeech 2005, 1669-1672, doi: 10.21437/Interspeech.2005-545

@inproceedings{scharenborg05b_interspeech,
  author={Odette Scharenborg and Stephanie Seneff},
  title={{Two-pass strategy for handling OOVs in a large vocabulary recognition task}},
  year=2005,
  booktitle={Proc. Interspeech 2005},
  pages={1669--1672},
  doi={10.21437/Interspeech.2005-545}
}