Recognition of OOV proper names in diachronic audio news

LVCSR based audio indexing approaches are preferred as they allow search, navigation, browsing and structuring of audio/video documents based on their content. A major challenge with LVCSR based indexing of diachronic audio data, for e.g. broadcast audio news, is OOV words and specifically OOV PNs which are very important for indexing applications. In this paper we propose an approach for recognition of OOV PNs in audio news documents using PNs extracted from collections of diachronic text news from the internet. The approach has two steps (a) reduce the long list of OOV PNs in the diachronic text corpus to a smaller list of OOV PNs which are relevant to the audio document, using probabilistic topic models (b) perform a phonetic search for the target OOV PNs with the reduced list of relevant OOV PNs. We evaluate our approach on French broadcast news videos published over a period of 6 months. Latent Dirichlet Allocation topic model is trained on diachronic text news to model PN-topic relationships and then to retrieve OOV PNs relevant to the audio document. Our proposed method retrieves up to 90% of the relevant OOV PNs by reducing the OOV PN search space to only 5% of the total OOV PNs. Phonetic search for target OOV PNs gives an F1-score up to 0.392.