Speech Emotion Recognition of Teachers in Classroom Teaching

Jie, Liang; Zhao, Xingke; Zhang, Zhaohui

doi:10.1109/ccdc49329.2020.9164823

Cited by 13 publications

(4 citation statements)

References 4 publications

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“…Academic emotion evaluation in online learning for boosting learner wellbeing [67]. Emotion recognition can be used for providing a personalized support as feedback to learners [54]- [57], [63], [64]; for enhancing efficient training [58]; for enhancing outcome learning and instruction quality [59]; for identifying learner comprehension [60]; for enhancing quality of teaching [65]; for extracting information from comment in online course learning [66]; for providing adaptable elearning [68]; for presenting adaptive course content [69]; for supporting teacher training [70]. FER2013, CK+ and JAFFE dataset consist of six basic emotions (Angry, Disgust, Fear, Happy, Sad, Surprise), and Neutral.…”

Section: The Functions Of Prolmentioning

confidence: 99%

A Brief Study of The Use of Pattern Recognition in Online Learning: Recommendation for Assessing Teaching Skills Automatically Online Based

Utami

Hartanto²,

Soesanti³

2022

ELINVO

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Online learning has become a trend for the current generation of students who have been exposed to advanced information and communication technology. Smart education can use pattern recognition. Manual assessments are subjective and inconsistent. To overcome these problems, pattern recognition can be used in the non-verbal aspect assessment system. This study describes pattern recognition in online learning about the functions, modalities, and algorithms and specifically related to the recognition system of non-verbal aspects of teaching skills. The literature study was carried out through the stages of planning, selection, extraction, and selection. There are 86 articles reviewed. The first result is the functions of implementing pattern recognition in online learning are engagement recognition, attention detection, emotion recognition, learning behavior, learning activity recognition, authentication, teaching training, etc. using four classifications of modality: visual, audio, biosignal, behavioral, and CNN as the most widely used learning algorithm. Secondly, all modalities (except behavioral) and CNN algorithm can be used for assessing teaching skills. Early development of the non-verbal aspect assessment system can use Facial Expression Recognition (FER) and Hand Gesture Recognition (HGR). The future analysis needs to focus on technology characteristics, the meaningfulness of the content, and the proper teaching mode. In the end, hopefully, prospective teachers will acquire technology that can make it easier for them to practice teaching and get objective assessments.

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Section: The Functions Of Prolmentioning

confidence: 99%

A Brief Study of The Use of Pattern Recognition in Online Learning: Recommendation for Assessing Teaching Skills Automatically Online Based

Utami

Hartanto²,

Soesanti³

2022

ELINVO

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“…The frequency (hertz) and amplitude of colour dimension are converted to log scale and decibel (dB) scale, respectively, to form the spectrogram over time. The mel spectrogram can visualise speech signals on the mel‐scale and generate various patterns [11, 14, 28] too train a machine learning (ML) or deep learning (DL)‐based classifier [29–31]. However, this method incorporates more computational complexity than the YAAPT algorithm.…”

Section: Introductionmentioning

confidence: 99%

Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification

Lin

Lai

Huang

et al. 2023

IET Signal Processing

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Human speech signals may contain specific information regarding a speaker's characteristics, and these signals can be very useful in applications involving interactive voice response (IVR) and automatic speech recognition (ASR). For IVR and ASR applications, speaker classification into different ages and gender groups can be applied in humanmachine interaction or computer-based interaction systems for customised advertisement, translation (text generation), machine dialog systems, or self-service applications. Hence, an IVR-based system dictates that ASR should function through users' voices (specific voice-frequency bands) to identify customers' age and gender and interact with a host system. In the present study, we intended to combine a pitch detection (PD)-based extractor and a voice classifier for gender identification. The Yet Another Algorithm for Pitch Tracking (YAAPT)-based PD method was designed to extract the voice fundamental frequency (F 0 ) from non-stationary speaker's voice signals, allowing us to achieve gender identification, by distinguishing differences in F 0 between adult females and males, and classify voices into adult and children groups. Then, in vowel voice signal classification, a one-dimensional (1D) convolutional neural network (CNN), consisted of a multi-round 1D kernel convolutional layer, a 1D pooling process, and a vowel classifier that could preliminary divide feature patterns into three level ranges of F 0 , including adult and children groups. Consequently, a classifier was used in the classification layer to identify the speakers' gender. The proposed PD-based extractor and voice classifier could reduce complexity and improve classification efficiency. Acoustic datasets were selected from the Hillenbrand database for experimental tests on 12 vowels classifications, and K-fold cross-validations were performed. The experimental results demonstrated that our

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“…Experimental results show that the improved model's mAP is increased by 4%, the F1 score is increased by 3.2%, and the detection time is reduced by 1/3. Liang et al [23] focused on teacher's voice signals and designs an emotion detection audio processing system. Teachers' speeches are used to determine their emotions.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition in Learning Scenes Supported by Smart Classroom and Its Application

Zhu,

Zheng,

et al. 2023

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Emotion recognition technology is one of the important applications of artificial intelligence and machine learning in the field of education. By recognizing the emotions of students in learning scenes, teachers can better understand the learning status of students and provide them with personalized learning resources and help. Current emotion recognition methods are mainly based on static facial emotions, neglecting the temporal features of facial emotions, which may lead to inaccurate recognition results. In order to overcome these challenges, this study conducts research on emotion recognition and its application in learning scenes supported by smart classrooms. The Transformer encoder is used to extract the temporal features of student facial emotions based on learning scenes, i.e., the selfattention module of the encoder is used to extract the temporal features of facial emotions in learning scenes. Residual attention networks, Transformers, and non-local neural networks are used to extract facial emotion features from different perspectives and levels. The combination of Vision-Transformer (ViT) and NetVLAD enables the model to learn the features of data from multiple perspectives, thereby improving the generalization ability of the model. The experimental results verify the effectiveness of the constructed model.

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Speech Emotion Recognition of Teachers in Classroom Teaching

Cited by 13 publications

References 4 publications

A Brief Study of The Use of Pattern Recognition in Online Learning: Recommendation for Assessing Teaching Skills Automatically Online Based

A Brief Study of The Use of Pattern Recognition in Online Learning: Recommendation for Assessing Teaching Skills Automatically Online Based

Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification

Emotion Recognition in Learning Scenes Supported by Smart Classroom and Its Application

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