Language modeling structures in audio transcription for retrieval of historical speeches

In this paper we apply speech recognition for automatic transcript generation for spoken document retrieval. The transcripts are used to compute an index for an archive of historical speeches and to provide the index, speech, and transcripts available for query based retrieval and browsing. In addition to acoustic variability, the task is challenging, because it covers a broad spectrum of different speaking styles and use of language. Language modeling is important for speech recognition to determine the prior probabilities of the compared word and sentence candidates in decoding. Various large text corpora are available in electronic format for language model training, but the open question is what and how should we include to improve the audio transcripts of this task. In this work we compare large overall language models to focused ones trained on selected subsets of the data, and to combinations between both. With respect to the potential index terms, improvements were obtained for transcripts that did not fit well to the scope of the large overall language model.