Kun-Yi Huang scite author profile

Latent structure models have been proposed in many applications. For space time health data it is often important to be able to find underlying trends in time which are supported by subsets of small areas. Latent structure modeling is one approach to this analysis. This paper presents a mixture-based approach that can be appied to component selction. The analysis of a Georgia ambulatory asthma county level data set is presented and a simulation-based evaluation is made.

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A chatbot using LSTM-based multi-layer embedding for elderly care

Huang

et al. 2017

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LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors

Huang

et al. 2018

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Detecting Unipolar and Bipolar Depressive Disorders from Elicited Speech Responses Using Latent Affective Structure Model

Huang

et al. 2020

IEEE Trans. Affective Comput.

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Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses

Huang

2019

Pattern Recognition

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Coupled HMM-based multimodal fusion for mood disorder detection through elicited audio–visual signals

Yang

Huang

et al. 2016

J Ambient Intell Human Comput

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Cell-Coupled Long Short-Term Memory With $L$ -Skip Fusion Mechanism for Mood Disorder Detection Through Elicited Audiovisual Features

Huang

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

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Kun-Yi Huang

Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds

Space–time latent component modeling of geo‐referenced health data

A chatbot using LSTM-based multi-layer embedding for elderly care

LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors

Detecting Unipolar and Bipolar Depressive Disorders from Elicited Speech Responses Using Latent Affective Structure Model

Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses

Coupled HMM-based multimodal fusion for mood disorder detection through elicited audio–visual signals

Cell-Coupled Long Short-Term Memory With $L$ -Skip Fusion Mechanism for Mood Disorder Detection Through Elicited Audiovisual Features

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