Effects of word string language models on noisy broadcast news speech recognition

In this paper, we present the results that our n-gram based word string language model, combined with speaker and noise adaptation of the acoustic model, improves recognition performance of noisy broadcast news speech. The focus was brought into a remedy against recognition errors of short words. The word string language models based on POS and n-gram frequency reduced deletion errors by 17%, insertion errors by 20%, and substitution errors by 3% in Japanese TV broadcast news speech recognition.