1. Language model using HTK
Raymond Sastraputera

2. Overview
Introduction
Implementation
Experimentation
Conclusion

3. Introduction Language model HTK 3.3 N-gram Windows binary Word 1

4. Implementation Database Preparation Mapping OOV words Word map
N-gram file
Mapping OOV words
Vocabulary list

5. Implementation Language model generation Perplexity Unigram Bigram
Trigram
4-gram
Perplexity

6. Result N-gram Perplexity unigram 401.7305 bigram 131.8727 trigram
4-gram

7. Conclusion and Summary
Higher n-gram
Less perplexity
More memory usage
Too high means over fitting
Multiple backed
Waste of time

8. Reference
1. HTK (

9. Thank you
Any Questions?