Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition

Li, Longfei; Zhao, Yong; Jiang, Dongmei; Zhang, Yanning; Wang, Fengna; González, Isabel; Enescu, Valentin; Sahli, Hichem

doi:10.1109/acii.2013.58

Cited by 121 publications

(59 citation statements)

References 11 publications

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“…Standard sets of HSFs and LLDs are given in eGeMAPS [121] and ComParE [123]. HMMs have long served as a standard choice for further modeling of temporal variation in speech signals, especially in ASR [155], but also in SER [125]. More recently, RNNs have emerged as a preferred way of modeling the sequential aspect of speech [156].…”

Section: Learning Temporal Features For Sermentioning

confidence: 99%

Deep Learning for Human Affect Recognition: Insights and New Developments

Rouast

Adam

Chiong

2021

IEEE Trans. Affective Comput.

147

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Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature representations, and (iii) joint feature representations for multimodal sensor data. Exemplary state-of-the-art architectures illustrate the progress. Our findings show the role deep architectures will play in human affect recognition, and can serve as a reference point for researchers working on related applications.

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Section: Learning Temporal Features For Sermentioning

confidence: 99%

Deep Learning for Human Affect Recognition: Insights and New Developments

Rouast

Adam

Chiong

2021

IEEE Trans. Affective Comput.

147

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“…In the works of Brueckner et al further related speaker states and traits from the ISCA Interspeech Computational Paralinguistics Challenges have been considered -often outperforming the best results obtained in those [ [3,4,5,6]]. Further examples for emotional speech recognition include [ [7,25,26,1,21,29]]. In a related way, deep learning has also been successfully applied to emotion recognition in music [ [31]].…”

Section: Deep Learningmentioning

confidence: 98%

Deep Learning Our Everyday Emotions

Schuller

2015

Advances in Neural Networks: Computational and Theoretical Issues

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Abstract. Emotion is omnipresent in our daily lives and has a significant influence on our functional activities. Thus, computer-based recognising and monitoring of affective cues can be of interest such as when interacting with intelligent systems, or for health-care and security reasons. In this light, this short overview focuses on audio/visual and textual cues as input feature modality for automatic emotion recognition. In particular, it shows how these can best be modelled in a Neural Network context. This includes deep learning, and sparse auto-encoders for transfer learning of a compact task and population representation. It further shows avenues towards massively autonomous rich multitasklearning and required confidence estimation as is needed to prepare such technology for real-life application.

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“…For performing well, efforts on audio fragment have been made by researchers (Attabi & Dumouchel, 2013;Bahreini et al, 2016;Li et al, 2013). However, they designed for general use, rather than for online learning with specific settings.…”

Section: E-learningmentioning

confidence: 99%

A review on sentiment discovery and analysis of educational big‐data

Han

Wu²,

Huang

et al. 2019

WIREs Data Min & Knowl

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Sentiment discovery and analysis (SDA) aims to automatically identify the underlying attitudes, sentiments, and subjectivity towards a certain entity such as learners and learning resources. Due to its enormous potential for smart education, SDA has been deemed as a powerful technique for identifying and classifying sentiments from multimodal and multisource data over the whole process of education. For big educational data streams, SDA faces challenges in unimodal feature selection, sentiment classification, and multimodal fusion. As such, a large body of studies in the literature explores diverse approaches to SDA for educational applications. This paper provides a self‐contained, uniform overview of the SDA techniques for education. In particular, we focus on prominent studies in unimodal sentiment features and classifications (e.g., text, audio, and visual). In addition, we present a novel SDA framework of multimodal fusions, together with description of their crucial components. Based on this framework, we review different approaches to SDA on education from the perspectives of approaches and applications. After comprehensively reviewing the SDA techniques on education, we present the trends and prospectives of the future SDA research under ubiquitous education contexts. This article is categorized under: Application Areas > Education and Learning

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Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition

Cited by 121 publications

References 11 publications

Deep Learning for Human Affect Recognition: Insights and New Developments

Deep Learning for Human Affect Recognition: Insights and New Developments

Deep Learning Our Everyday Emotions

A review on sentiment discovery and analysis of educational big‐data

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