Story segmentation for speech transcripts in sparse data conditions

Information Retrieval systems determine relevance by comparing information needs with the content of potential retrieval units. Unlike most textual data, automatically generated speech transcripts cannot by default be easily divided into obvious retrieval units due to a lack of explicit structural markers. This problem can be addressed by automatically detecting topically cohesive segments, or stories. However, when the content collection consists of speech from less formal domains than broadcast news, most of the standard automatic boundary detection methods are potentially unsuitable due to their reliance on learned features. In particular for conversational speech, the lack of adequate training data can present a significant issue. In this paper four methods for automatic segmentation of speech transcriptions are compared. These are selected because of their independence from collection specific knowledge and implemented without the use of training data. Two of the four methods are based on existing algorithms, the others are novel approaches based on a dynamic segmentation algorithm (QDSA) that incorporates information about the query, and WordNet. Experiments were done on a task similar to TREC SDR unknown boundaries condition. For the best performing system, QDSA, the retrieval scores for a tfidf-type ranking function were equivalent to a reference segmentation, and improved through document length normalization using the bm25/Okapi method. For the task of automatically segmenting speech transcripts for use in information retrieval, we conclude that a training-poor processing paradigm which can be crucial for handling surprise data is feasible.