Source-extended language model for large vocabulary continuous speech recognition

Information source extension is utilized to improve the language model for large vocabulary continuous speech recognition (LVCSR). McMillan's theory, source extension make the model entropy close to the real source entropy, implies that the better language model can be obtained by source extension (making new unit through word concatenations and using the new unit for the language modeling). In this paper, we examined the e ectiveness of this source extension. Here, we tested two methods of source extension: frequency-based extension and entropy-based extension. We tested the e ect in terms of perplexity and recognition accuracy using Mainichi newspaper articles and JNAS speech corpus. As the results, the bi-gram perplexity is improved from 98.6 to 70.8 and tri-gram perplexity is improved from 41.9 to 26.4. The bigram-based recognition accuracy is improved from 79.8% to 85.3%.