Woan-Shiuan Chien

• Doctor of Engineering
• PhD Student at National Tsing Hua University

Renal cell carcinoma tumor images are utilized in various fields for critical functions. Application is constrained in unique scenarios requiring specific tumor imaging, which is often difficult to obtain due to rarity or privacy concerns. While general tumor synthesis has been successful as a data acquisition solution, specific domains demand prec...

Accurate stress detection from physiological signals is often complicated by individual identity traits, which must first be identified before they can be effectively removed to improve model performance. To address this, we propose a method that combines Detrended Fluctuation Analysis (DFA) and Augmented Dickey-Fuller (ADF) Analysis to extract sta...

Studies show that individual attributes such as age and gender significantly influence physiological responses and their correlation with stress, often forming complex and overlapping relationships. These attributes are essential for enhancing physiological signal-based stress detection. Our work leverages hypergraph in multi-attribute representati...

Speech emotion recognition (SER) is a vital component in various everyday applications. Cross-corpus SER models are increasingly recognized for their ability to generalize performance. However, concerns arise regarding fairness across demographics in diverse corpora. Existing fairness research often focuses solely on corpus-specific fairness, negle...

The advancement of Speech Emotion Recognition (SER) is significantly dependent on the quality of emotional speech corpora used for model training. Researchers in the field of SER have developed various corpora by adjusting design parameters to enhance the reliability of the training source. For this study, we focus on exploring communication mode...

Advanced wearable tracking shows potential for identifying psychological and emotional stress relevant to the mental health of high-intensity emergency responders. Heart rate variability (HRV) captured by wearable devices can indicate the correlation between intra-subject daily variations and stress. HRV also varies due to various demographic attri...

Heart Rate Variability (HRV) features are recognized as powerful indicators of various diseases, including heart failure, diabetes, and mental health disorders. Besides, HRV features are robust against noise, making them ideal for wearable devices. Despite their potential, HRV feature sets are limited by sample quantity. Direct augmentation often d...

Mental stress has become a growing concern in contemporary society; fortunately, recent developments in wear-able technology now offer a promising solution. However, a common issue in longitudinal tracking with wearable sensors is missing data, which can introduce biases during model training, affecting predictions and leading to unfair outcomes fo...

Speech emotion recognition (SER) has been extensively integrated into voice-centric applications. A unique fairness issue of SER stems from the naturally biased labels given by raters as ground truth. While existing efforts primarily aim to advance SER fairness through a group (i.e., gender) fairness standpoint, our analysis reveals that label bias...

Speech emotion recognition (SER) helps to achieve better human-to-machine interactions in voice technologies. Recent studies have pointed out critical fairness issues in the SER. While there are efforts in building fair SER, most of the works focus on fairness between demographic groups and rely on these broad categorical attributes to build a fair...

Automatic sensing of emotional information in speech is important for numerous everyday applications. Conventional Speech Emotion Recognition (SER) models rely on averaging or consensus of human annotations for training, but emotions and raters' interpretations are subjective in nature, leading to diverse variations in perceptions. To address this,...

Speech emotion recognition (SER) adds to the humane aspects of voice technologies to enhance user experiences. The ground truth emotion annotations provided by human raters and attributes related to the speakers themselves arise a compounded fairness issue in SER. While there exist works in fair SER, our work presents one of the first studies in ad...

Continuously identifying day-to-day mental stress can be realized by accessing wearable devices to measure physiological indicators. However, the nature of bodily signals raises issues of privacy and data heterogeneity. Recent federated learning scheme provides a promising direction to alleviate the privacy concern, but the large inter-client diffe...

The field of speech emotion recognition (SER) aims to create scientifically rigorous systems that can reliably characterize emotional behaviors expressed in speech. A key aspect for building SER systems is to obtain emotional data that is both reliable and reproducible for practitioners. However, academic researchers encounter difficulties in acces...

Speech emotion recognition (SER) is a key technological module to be integrated into many voice-based solutions. One of the unique fairness issues in SER is caused by the inherently biased emotion perception given by the raters as ground truth labels. Mitigating rater biases are at core for SER to move toward optimizing both recognition and fairnes...

Modeling cross-lingual speech emotion recognition (SER) has become more prevalent because of its diverse applications. Existing studies have mostly focused on technical approaches that adapt the feature, domain, or label across languages, without considering in detail the similarities between the languages. This study focuses on domain adaptation i...

Advancing speech emotion recognition (SER) depends highly on the source used to train the model, i.e., the emotional speech corpora. By permuting different design parameters, researchers have released versions of corpora that attempt to provide a "better-quality" source for training SER. In this work, we focus on studying communication modes of col...

Physiological synchrony is a particular phenomenon of physiological responses during a face-face conversation. However, while many previous studies had proposed various physiological synchrony measures between interlocutors in dyadic conversations, there are very few works on computing physiological synchrony in small groups (three or more people)....

Individual (personalized) self-assessed emotion recognition has received more and more attention recently, such as Human-Centered Artificial Intelligence (AI). In most previous studies, researchers utilized the physiological changes and reactions in the body evoked by multi-media stimuli, e.g., video or music, to build a model for recognizing indiv...

It is well known that human is not good at deception detection because of a natural inclination of truth-bias. However, during a conversation, when an interlocutor (interrogator) is being asked explicitly to assess whether his/her interacting partner (deceiver) is lying, this perceptual judgment depends highly on how the interrogator interprets the...

Affective media videos have been used as stimulus to investigate an individual's affective-physio responses. In this study, we aim to develop a network learning strategy for robust cross-corpus emotion recognition using physiological features jointly with affective video content. Specifically, we present a novel framework of Visual Semantic Graph L...

The objective of this study is to establish an efficient and effective recognition system for myocardial ischemic and myocardial infarction episodes in ECG. We first applied a preprocessing algorithm to reduce noise and baseline wander. Then, we simplified the procedures of identifying the important points and defined these points based only on hea...