Adding punctuation and capitalization greatly improves the readability of automatic speech transcripts. We discuss an approach for performing both tasks in a single pass using a purely text-based n-gram language model. We study the effect on performance of varying the n-gram order (from n = 3 to n = 6) and the amount of training data (from 58 million to 55 billion tokens). Our results show that using larger training data sets consistently improves performance, while increasing the n-gram order does not help nearly as much.
REF examined the effect of different orders of n-grams on performance in punctuation prediction.
Restoring punctuation and capitalization in transcribed speech
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Computer Science