Topic-Specific Language Model Based on Graph Spectral Approach for Speech Recognition
Large vocabulary continuous speech recognition techniques have greatly advanced in recent years due to the remarkable advances of computers. Even personal computers today have extraordinary computation powers so that we can perform automatic speech recognition with high performance in a small computer. This is due to not only the evolution of the computers but also the development of some efficient recognition algorithms and the utilization of statistical acoustic and language models with a large speech database.
In addition, rapid development of the WWW makes it possible to utilize enormous textual data resources for creating excellent language models. Especially, the topic-specific language model can give high performance for speech recognition if the large amount of appropriate topic-related documents can be collected.
Under these circumstances, we have been developing the broadcast news search system with the language model adaptation using the information on the WWW. The basic idea is that broadcast news has similar Web documents on the Internet news site, so the performance of news speech recognition can be improved with the adapted language model by collecting a similar article via Web crawling [1, 2].
KeywordsSpeech Recognition Spectral Cluster Recognition Result Index Term Broadcast News
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