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. 1 Introduction Recently large vocabulary continuous speech recognition systems have been challenged with increasingly difficult tasks, as the research focus has shifted from read speech data to speech data found in the real world. The Japanese counterpart of HUB-4 [1] broadcast news recognition projects have been pursued by NHK (Japan Broadcasting Corporation) and other research institutes since 1996 [2, 3]. The research has now stepped in the stage where real time captioning of news speech [..