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Non-verbal behavior is a central challenge in understanding the dynamics of a conversation and the affective states between interlocutors arising from the interaction. Although psychological research has demonstrated that non-verbal behaviors vary across cultures, limited computational analysis has been conducted to clarify these differences and assess their impact on engagement recognition. To gain a greater understanding of engagement and non-verbal behaviors among a wide range of cultures and language spheres, in this study we conduct a multilingual computational analysis of non-verbal features and investigate their role in engagement and engagement prediction. To achieve this goal, we first expanded the NoXi dataset, which contains interaction data from participants living in France, Germany, and the United Kingdom, by collecting session data of dyadic conversations in Japanese and Chinese, resulting in the enhanced dataset NoXi+J. Next, we extracted multimodal non-verbal features, including speech acoustics, facial expressions, backchanneling and gestures, via various pattern recognition techniques and algorithms. Then, we conducted a statistical analysis of listening behaviors and backchannel patterns to identify culturally dependent and independent features in each language and common features among multiple languages. These features were also correlated with the engagement shown by the interlocutors. Finally, we analyzed the influence of cultural differences in the input features of LSTM models trained to predict engagement for five language datasets. A SHAP analysis combined with transfer learning confirmed a considerable correlation between the importance of input features for a language set and the significant cultural characteristics analyzed., Comment: 8 pages. 6 figures. International Conference on Multimodal Interaction, November 4-8, 2024, San Jose, Costa Rica

Room acoustic parameters (RAPs) and room physical parameters ( RPPs) are essential metrics for parameterizing the room acoustical characteristics (RAC) of a sound field around a listener's local environment, offering comprehensive indications for various applications. The current RAPs and RPPs estimation methods either fall short of covering broad real-world acoustic environments in the context of real background noise or lack universal frameworks for blindly estimating RAPs and RPPs from noisy single-channel speech signals, particularly sound source distances, direction-of-arrival (DOA) of sound sources, and occupancy levels. On the other hand, in this paper, we propose a novel universal blind estimation framework called the blind estimator of room acoustical and physical parameters (BERP), by introducing a new stochastic room impulse response (RIR) model, namely, the sparse stochastic impulse response (SSIR) model, and endowing the BERP with a unified encoder and multiple separate predictors to estimate RPPs and SSIR parameters in parallel. This estimation framework enables the computationally efficient and universal estimation of room parameters by solely using noisy single-channel speech signals. Finally, all the RAPs can be simultaneously derived from the RIRs synthesized from SSIR model with the estimated parameters. To evaluate the effectiveness of the proposed BERP and SSIR models, we compile a task-specific dataset from several publicly available datasets. The results reveal that the BERP achieves state-of-the-art (SOTA) performance. Moreover, the evaluation results pertaining to the SSIR RIR model also demonstrated its efficacy. The code is available on GitHub., Comment: 13-page, Submitted to IEEE/ACM Transaction on Audio Speech and Language Processing (TASLP)

The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies. Like traditional machine learning methods, speech-based automatic detection also suffers from data privacy risks because the data of specific patients are exclusive to each medical institution. A common practice is to use federated learning to protect the patients' data privacy. However, its distributed learning process also causes performance reduction. To alleviate this problem while protecting user privacy, we propose a federated contrastive pre-training (FedCPC) performed before federated training for AD speech detection, which can learn a better representation from raw data and enables different clients to share data in the pre-training and training stages. Experimental results demonstrate that the proposed methods can achieve satisfactory performance while preserving data privacy., Comment: accepted in IEEE-ASRU2023

Multimodal sentiment analysis is an important area for understanding the user's internal states. Deep learning methods were effective, but the problem of poor interpretability has gradually gained attention. Previous works have attempted to use attention weights or vector distributions to provide interpretability. However, their explanations were not intuitive and can be influenced by different trained models. This study proposed a novel approach to provide interpretability by converting nonverbal modalities into text descriptions and by using large-scale language models for sentiment predictions. This provides an intuitive approach to directly interpret what models depend on with respect to making decisions from input texts, thus significantly improving interpretability. Specifically, we convert descriptions based on two feature patterns for the audio modality and discrete action units for the facial modality. Experimental results on two sentiment analysis tasks demonstrated that the proposed approach maintained, or even improved effectiveness for sentiment analysis compared to baselines using conventional features, with the highest improvement of 2.49% on the F1 score. The results also showed that multimodal descriptions have similar characteristics on fusing modalities as those of conventional fusion methods. The results demonstrated that the proposed approach is interpretable and effective for multimodal sentiment analysis., Comment: 4 tables, 4 figures

This paper focuses on the estimation of a driver's psychological characteristics using driving data for driving assistance systems. Driving assistance systems that support drivers by adapting individual psychological characteristics can provide appropriate feedback and prevent traffic accidents. As a first step toward implementing such adaptive assistance systems, this research aims to develop a model to estimate drivers' psychological characteristics, such as cognitive function, psychological driving style, and workload sensitivity, from on-road driving behavioral data using machine learning and deep learning techniques. We also investigated the relationship between driving behavior and various cognitive functions, including the Trail Making Test (TMT) and Useful Field of View (UFOV) test, through regression modeling. The proposed method focuses on road type information and captures various durations of time-series data observed from driving behaviors. First, we segment the driving time-series data into two road types, namely, arterial roads and intersections, to consider driving situations. Second, we further segment data into many sequences of various durations. Third, statistics are calculated from each sequence. Finally, these statistics are used as input features of machine learning models to estimate psychological characteristics. The experimental results show that our model can estimate a driver's cognitive function, namely, the TMT~(B) and UFOV test scores, with Pearson correlation coefficients $r$ of 0.579 and 0.708, respectively. Some characteristics, such as psychological driving style and workload sensitivity, are estimated with high accuracy, but whether various duration segmentation improves accuracy depends on the characteristics, and it is not effective for all characteristics.

This paper describes a personality-adaptive multimodal dialogue system developed for the Dialogue Robot Competition 2022. To realize a dialogue system that adapts the dialogue strategy to individual users, it is necessary to consider the user's nonverbal information and personality. In this competition, we built a prototype of a user-adaptive dialogue system that estimates user personality during dialogue. Pretrained DNN models are used to estimate user personalities annotated as Big Five scores. This model is embedded in a dialogue system to estimate user personality from face images during the dialogue. We proposed a method for dialogue management that changed the dialogue flow based on the estimated personality characteristics and confirmed that the system works in a real environment in the preliminary round of this competition. Furthermore, we implemented specific modules to enhance the multimodal dialogue experience of the user, including personality assessment, controlling facial expressions and movements of the android, and dialogue management to explain the attractiveness of sightseeing spots. The aim of dialogue based on personality assessment is to reduce the nervousness of users, and it acts as an ice breaker. The android's facial expressions and movements are necessary for a more natural android conversation. Since the task of this competition was to promote the appeal of sightseeing spots and to recommend an appropriate sightseeing spot, the dialogue process for how to explain the attractiveness of the spot is important. All results of the subjective evaluation by users were better than those of the baseline and other systems developed for this competition. The proposed dialogue system ranked first in both "Impression Rating" and "Effectiveness of Android Recommendations". According to the total evaluation in the competition, the proposed system was ranked first overall., Comment: This paper is part of the proceedings of the Dialogue Robot Competition 2022

As the importance of intrusion detection and prevention systems (IDPSs) increases, great costs are incurred to manage the signatures that are generated by malicious communication pattern files. Experts in network security need to classify signatures by importance for an IDPS to work. We propose and evaluate a machine learning signature classification model with a reject option (RO) to reduce the cost of setting up an IDPS. To train the proposed model, it is essential to design features that are effective for signature classification. Experts classify signatures with predefined if-then rules. An if-then rule returns a label of low, medium, high, or unknown importance based on keyword matching of the elements in the signature. Therefore, we first design two types of features, symbolic features (SFs) and keyword features (KFs), which are used in keyword matching for the if-then rules. Next, we design web information and message features (WMFs) to capture the properties of signatures that do not match the if-then rules. The WMFs are extracted as term frequency-inverse document frequency (TF-IDF) features of the message text in the signatures. The features are obtained by web scraping from the referenced external attack identification systems described in the signature. Because failure needs to be minimized in the classification of IDPS signatures, as in the medical field, we consider introducing a RO in our proposed model. The effectiveness of the proposed classification model is evaluated in experiments with two real datasets composed of signatures labeled by experts: a dataset that can be classified with if-then rules and a dataset with elements that do not match an if-then rule. In the experiment, the proposed model is evaluated. In both cases, the combined SFs and WMFs performed better than the combined SFs and KFs. In addition, we also performed feature analysis., Comment: 9 pages, 5 figures, 3 tables