@inproceedings{9b995d3bc52445108dbe2457dae42fc2,
title = "Comparing Hybrid NN-HMM and RNN for Temporal Modeling in Gesture Recognition",
abstract = "This paper provides an extended comparison of two temporal models for gesture recognition, namely Hybrid Neural Network-Hidden Markov Models (NN-HMM) and Recurrent Neural Networks (RNN) which have lately claimed the state-the-art performances. Experiments were conducted on both models in the same body of work, with similar representation learning capacity and comparable computational costs. For both solutions, we have integrated recent contributions to the model architectures and training techniques. We show that, for this task, Hybrid NN-HMM models remain competitive with Recurrent Neural Networks in a standard setting. For both models, we analyze the influence of the training objective function on the final evaluation metric. We further tested the influence of temporal convolution to improve context modeling, a technique which was recently reported to improve the accuracy of gesture recognition.",
keywords = "End-to-End learning, Gesture recognition, Hybrid NN-HMM, RNN, Representation learning",
author = "Nicolas Granger and \{El Yacoubi\}, \{Moun{\^i}m A.\}",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-70096-0\_16",
language = "English",
isbn = "9783319700953",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "147--156",
editor = "Dongbin Zhao and El-Alfy, \{El-Sayed M.\} and Derong Liu and Shengli Xie and Yuanqing Li",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
}