Abstract
Language models for speech recognition concentrate solely on recognizing the words that were spoken. In this paper, we advocate redefining the speech recognition problem so that its goal is to find both the best sequence of words and their POS tags, and thus incorporate POS tagging. To use POS tags effectively, we use clustering and decision tree algorithms, which allow generalizations between POS tags and words to be effectively used in estimating the probability distributions. We show that our POS model gives a reduction in word error rate and perplexity for the Trains corpus in comparison to word and class-based approaches. By using the Wall Street Journal corpus, we show that this approach scales up when more training data is available.
| Original language | English (US) |
|---|---|
| Pages | 129-137 |
| Number of pages | 9 |
| State | Published - 1999 |
| Event | 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, EMNLP 1999 - College Park, United States Duration: Jun 21 1999 → Jun 22 1999 |
Conference
| Conference | 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, EMNLP 1999 |
|---|---|
| Country/Territory | United States |
| City | College Park |
| Period | 6/21/99 → 6/22/99 |
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
- Computational Theory and Mathematics