REMAP for video soundtrack indexing

Indexing of video soundtracks is an important issue for the navigation in multimedia databases. Based on wordspotting techniques, it should meet very constraining specifications; namely fast response to queries, concise processed speech information for limiting the storage memory, speaker independant mode, easy characterization of any word by its phonemic spelling. A solution based on phonemic lattices and on a division of the indexing process into an off-line and an online part is proposed in this paper. Previous works [1][2] based on frame labelling and Maximum Likelihood criterion are now modified to take into account this new approach based on a Maximum a Posteriori (MAP) criterion. The REMAP algorithm [3] implements this MAP criterion for training. It has several avantages such as maximizing the global discriminant criterion, avoiding the difficult problem of phoneme transition detection during the training process and being well suited for a hybrid Hidden Markov Model (HMM) and Neural Network (NN) approach.