Abstract
One challenge in automated speech recognition is to determine domain-specific vocabulary like names, brands, technical terms etc. by using generic language models. Especially in broadcast news new names occur frequently. We present an unsupervised method for a language model adaptation, which is used in automated speech recognition with a two-pass decoding strategy to improve spoken document retrieval on broadcast news. After keywords are extracted from each utterance, a web resource is queried to collect utterance-specific adaptation data. This data is used to augment the phonetic dictionary and adapt the basic language model. We evaluated this strategy on a data set of summarized German broadcast news using a basic retrieval setup.
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Wilhelm-Stein, T., Herms, R., Ritter, M., Eibl, M. (2014). Improving Transcript-Based Video Retrieval Using Unsupervised Language Model Adaptation. In: Kanoulas, E., et al. Information Access Evaluation. Multilinguality, Multimodality, and Interaction. CLEF 2014. Lecture Notes in Computer Science, vol 8685. Springer, Cham. https://doi.org/10.1007/978-3-319-11382-1_11
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DOI: https://doi.org/10.1007/978-3-319-11382-1_11
Publisher Name: Springer, Cham
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