Improving spoken document retrieval by unsupervised language model adaptation using utterance-based web search
نویسندگان
چکیده
Information retrieval systems facilitate the search for annotated audiovisual documents from different corpora. One of the main problems is to determine domain-specific vocabulary like names, brands, technical terms etc. by using general language models (LM) especially in broadcast news. Our approach consists of two steps to overcome the out-of-vocabulary (OOV) problem to improve the spoken document retrieval performance. Therefore, we first separate the resulting transcript of a speech recognizer into blocks.
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