Improved recognition of Hungarian call center conversations

This paper summarizes our recent efforts made to automatically transcribe call center conversations in real-time. Data sparseness issue is addressed due to the small amount of transcribed training data. Accordingly, first the potentials in the inclusion of additional non-conventional training texts are investigated, and then morphological language models are introduced to handle data insufficiency. The baseline system is also extended with explicit models for non-verbal speech events such as hesitation or consent. In addition, all the above techniques are efficiently combined in the final system. The benefit of each approach is evaluated on real-life call center recordings. Results show that by utilizing morphological language models, significant error rate reduction can be achieved over the word baseline system, which is preserved across experimental setups. The results can be further improved if nonverbal events are also modeled.