Various studies highlighted that topic-based approaches give a powerful spoken content representation of documents. Nonetheless, these documents may contain more than one main theme, and their automatic transcription inevitably contains errors. In this study, we propose an original and promising framework based on a compact representation of a textual document, to solve issues related to topic space granularity. Firstly, various topic spaces are estimated with different numbers of classes from a Latent Dirichlet Allocation. Then, This multiple topic space representation is compacted into an elementary segment, called c-vector, originally developed in the context of speaker recognition. Experiments are conducted on the DECODA corpus of conversations. Results show the effectiveness of the proposed multi-view compact representation paradigm. Our identification system reaches an accuracy of 85%, with a significant gain of 9 points compared to the baseline (best single topic space configuration).
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