Abstract

Recent studies indicate that bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks are well-suited for automatic emotion recognition systems and may lead to better results than systems applying other widely used classifiers such as Support Vector Machines or feedforward Neural Networks. The good performance of BLSTM emotion recognition systems could be attributed to their ability to model and exploit contextual information self-learned via recurrently connected memory blocks which allows them to incorporate information about how emotion evolves over time. However, the actual amount of bidirectional context that a BLSTM classifier takes into account when classifying an observation has not been investigated so far. This paper presents a methodology to systematically investigate the number of past and future utterance-level observations that are considered to generate an emotion …

Date

2012

Authors

Martin Wöllmer, Angeliki Metallinou, Nassos Katsamanis, Björn Schuller, Shrikanth Narayanan

Conference

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

4157-4160

Publisher

IEEE