Improving Spoken Document Retrieval by. Unsupervised Language Model Adaptation Using Utterance-based Web Search

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. Keywords are extracted from each transcribed utterance of a block for the search of web resources in an unsupervised manner in order to obtain adaptation data. These data are used to perform a block-specific adaptation of a general pronunciation dictionary and a general LM. The second step comprises the utilization of a certain adapted dictionary and LM in the speech recognizer to improve the vocabulary coverage and to regard the perplexity for a corresponding block at once. We evaluate this strategy on a dataset of summarized German broadcast news. Our experimental results show improvements of up to 11.7% for MAP of 18 different topics and 7.5% of WER in comparison to the base LM.