A combined LSTM-RNN - HMM - Approach for meeting event segmentation and recognition

Automatic segmentation and classification of recorded meetings provides a basis that enables effective browsing and querying in a meeting archive. Yet, robustness of today's approaches is often not reliable enough. We therefore strive to improve on this task by introduction of a tandem approach combining the discriminative abilities of recurrent neural nets and warping capabilities of hidden markov models. Thereby long short-term memory cells are used for audio-visual frame analysis within the neural net. These help to overcome typical long time lags. Extensive test runs on the public M4 Scripted Meeting Corpus show great performance applying our suggested novel approach.