No abstract

Automatic emotion recognition is an active research topic with wide range of applications. Due to the high manual annotation cost and inevitable label ambiguity, the development of emotion recognition dataset is limited in both scale and quality. Therefore, one of the key challenges is how to build effective models with limited data resource. Previous works have explored different approaches to tackle this challenge including data enhancement, transfer learning, and semi-supervised learning etc. However, the weakness of these existing approaches includes such as training instability, large performance loss during transfer, or marginal improvement. In this work, we propose a novel semi-supervised multi-modal emotion recognition model based on cross-modality distribution matching, which leverages abundant unlabeled data to enhance the model training under the assumption that the inner emotional status is consistent at the utterance level across modalities. We conduct extensive experiments to evaluate the proposed model on two benchmark datasets, IEMOCAP and MELD. The experiment results prove that the proposed semi-supervised learning model can effectively utilize unlabeled data and combine multi-modalities to boost the emotion recognition performance, which outperforms other state-of-the-art approaches under the same condition. The proposed model also achieves competitive capacity compared with existing approaches which take advantage of additional auxiliary information such as speaker and interaction context. CCS Concepts• Computing methodologies → Semi-supervised learning settings; Semantic networks; • Human-centered computing → HCI design and evaluation methods.

This paper presents a learning self-tuning (LSTR) regulator which improves the tracking performance of itself while performing repetitive tasks. The controller is a self-tuning regulator based on learning parameter estimation. Experimentally, the controller was used to control the movement of a nonlinear piezoelectric actuator which is a part of the tool positioning system for a diamond turning lathe. Experimental results show that the controller is able to reduce the tracking error through the repetition of the task.

A prominent effect of the interface potential ͑IP͒ ͓E. L. Ivchenko and A. Yu. Kaminski, Phys. Rev. B 54, 5852 ͑1996͒; O. Krebs and P. Voisin, Phys. Rev. Lett. 77, 1829 ͑1996͔͒, the optical anisotropy of the forbidden transitions in quantum wells has been observed by reflectance-difference spectroscopy. Predictions by the heavy-light-hole coupling IP models are qualitatively consistent with all the observed features of the forbidden and the allowed transitions. The fact that the predicted value of the relative transition strength, which depends on neither the IP strength nor the electric field, disagrees with the observed one indicates that coupling involving X and/or L bands may also be important.