Leveraging Twitter for Low-Resource Conversational Speech Language Modeling
TABLE III: Perplexity of Word Class Language Models
['[BOLD] Language', '[BOLD] In-Domain Baseline', '[BOLD] Twitter Baseline', '[BOLD] Hybrid Method']
For each of the three languages, the hybrid method has a substantially lower perplexity than either of the baselines. The average perplexity improvement is more than 10%. This validates our hypothesis that the word classes learned from the Twitter data would generalize well. As a result, the perplexities are much larger. However, if more data is downloaded the size of the queue will expand and the predictive ranking will continue to have an advantage. In a real-world application, the predictive ranking model would be retrained as additional batches of users are downloaded for maximum performance.