[3N3-J-10] Vision, speech: voice and communication

[3N3-J-10-01] Multilingual Imputation Using Transfer Learning for Estimating Emotion from Speech

〇Koichi Sakaguchi¹, Shohei Kato^1,2 (1. Dept. of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology, 2. Frontier Research Institute for Information Science, Nagoya Institute of Technology)

Keywords:Multilingual Imputation, emotional speech data

Recently, vocal communication robots attract people thanks to development of AI and robot engineering. The
technology of estimating emotion from speech is important to realize a smooth dialog between human and robots.
This technology needs a large number of emotional speech data, but it is difficult to collect such data a lot. We
investigated the effectiveness of multilingual imputation by transfer learning using 1D convolutional bidirectional
LSTM. In this paper, we report the result. The result is suggested that increasing the number of languages of
emotional speech learned may exceed the performance of the model learned insufficient emotional speech in single language.