Abstract
The addressee detection problem arises in real spoken dialogue systems (SDSs) which are supposed to distinguish the speech addressed to them from the speech addressed to real humans. In this work, several modalities were analyzed, and acoustic data has been chosen as the main modality by reason of the most flexible usability in modern SDSs. To resolve the problem of addressee detection, deep learning methods such as fully-connected neural networks and Long Short-Term Memory were applied in the present study. The developed models were improved by using different optimization methods, activation functions and a learning rate optimization method. Also the models were optimized by using a recursive feature elimination method and multiple initialization to increase the training speed. A fully-connected neural network reaches an average recall of 0.78, a Long Short-Term Memory neural network shows an average recall of 0.65. Advantages and disadvantages of both architectures are provided for the particular task.
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Acknowledgments
This work is partially supported by the grant of the President of Russia (No. MD-254.2017.8) and by the RFBR (project No. 16-37-60100).
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Pugachev, A., Akhtiamov, O., Karpov, A., Minker, W. (2018). Deep Learning for Acoustic Addressee Detection in Spoken Dialogue Systems. In: Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2017. Communications in Computer and Information Science, vol 789. Springer, Cham. https://doi.org/10.1007/978-3-319-71746-3_4
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DOI: https://doi.org/10.1007/978-3-319-71746-3_4
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