Leveraging Twitter for Low-Resource Conversational Speech Language Modeling
TABLE IV: Turkish LM Interpolation Results
['[BOLD] Model', '[BOLD] Babel', '[BOLD] Babel + Twitter']
Specifically we were interested in finding whether there is an advantage in interpolating the class n-gram model with our word-based language models and how much each language model contributes to the result. Although the class-based LM by itself is worse than the Twitter word-based LM, the word + class combination LM is substantially better than any of the individual models. In the interpolated combination, the class LM received 38% of the weight and the Babel + Twitter word LM received 62% of the weight. Using the same technique of combining a word LM with a class LM without the Twitter data gives only a small 3% reduction in perplexity. The 19% reduction in perplexity over the Babel word trigram model, obtained by the Twitter word+class combination LM, comes from the superior class assignments learned on the Twitter data.