Investigation of the Effect of Increased Dimension Levels in Speech Emotion Recognition
IEEE Access, 2022 In human-machine interaction systems, speech emotion recognition plays a key role. Recognition of categorical emotions has made a great improvement during the last few decades, but emotion recognition of spontaneous speech is still very challenging. This
Haiyan Wang, Xiaohui Zhao, Yanping Zhao
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Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition [PDF]
IEEE Transactions on Affective Computing, 2023 This article presents a paradigm that adapts general large-scale pretrained models (PTMs) to speech emotion recognition task. Although PTMs shed new light on artificial general intelligence, they are constructed with general tasks in mind, and thus ...
Weidong Chen +3 more
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Speech emotion recognition method in educational scene based on machine learning
EAI Endorsed Transactions on Scalable Information Systems, 2022 In order to effectively improve the accuracy and anti noise performance of speech emotion recognition in educational scenes, a new method based on machine learning is studied. Based on the fundamental frequency and resonance degree, the speech emotional
Yanning Zhang, Gautam Srivastava
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Exploring Wav2vec 2.0 Fine Tuning for Improved Speech Emotion Recognition [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021 While Wav2Vec 2.0 has been proposed for speech recognition (ASR), it can also be used for speech emotion recognition (SER); its performance can be significantly improved using different fine-tuning strategies. Two baseline methods, vanilla fine-tuning (V-
Li-Wei Chen, Alexander I. Rudnicky
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PEFT-SER: On the Use of Parameter Efficient Transfer Learning Approaches For Speech Emotion Recognition Using Pre-trained Speech Models [PDF]
Affective Computing and Intelligent Interaction, 2023 Many recent studies have focused on fine-tuning pretrained models for speech emotion recognition (SER), resulting in promising performance compared to traditional methods that rely largely on low-level, knowledge-inspired acoustic features.
Tiantian Feng, Shrikanth S. Narayanan
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Foundation Model Assisted Automatic Speech Emotion Recognition: Transcribing, Annotating, and Augmenting [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023 Significant advances are being made in speech emotion recognition (SER) using deep learning models. Nonetheless, training SER systems remains challenging, requiring both time and costly resources.
Tiantian Feng, Shrikanth S. Narayanan
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Emotional Interactive Simulation System of English Speech Recognition in Virtual Context
Complexity, 2020 With the development of virtual scenes, the degree of simulation and functions of virtual reality have been very complete, providing a new platform and perspective for teaching design.
Dan Li
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Speech Emotion Recognition Using Self-Supervised Features [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022 Self-supervised pre-trained features have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of speech emotion recognition (SER) still need further investigation.
E. Morais +5 more
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IMPROVED SPEAKER-INDEPENDENT EMOTION RECOGNITION FROM SPEECH USING TWO-STAGE FEATURE REDUCTION
Journal of ICT, 2015 In the recent years, researchers are focusing to improve the accuracy of speech emotion recognition. Generally, high emotion recognition accuracies were obtained for two-class emotion recognition, but multi-class emotion recognition is still a ...
Hasrul Mohd Nazid +3 more
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Speech Emotion Recognition Based on Multiple Acoustic Features and Deep Convolutional Neural Network
Electronics, 2023 Speech emotion recognition (SER) plays a vital role in human–machine interaction. A large number of SER schemes have been anticipated over the last decade.
K. Bhangale, Mohanaprasad Kothandaraman
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