Efficient emotion recognition from speech using deep learning on spectrograms
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A model of saliency-based visual attention for rapid scene analysis
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Emotion Recognition from Speech Using wav2vec 2.0 Embeddings
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Learning Discriminative Features from Spectrograms Using Center Loss for Speech Emotion Recognition
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Speech Emotion Recognition via an Attentive Time–Frequency Neural Network
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“In detail, following the works in [8], [9], and [18], speech utterances are all resampled to 16000 Hz and divided into small segments with 128 frames (i.e., 20 ms), which can not only preserve the completeness of speech emotion but also augment the dataset.”
BackgroundMethodInfluentialLiu, F., Shen, S.-Y., Fu, Z.-W., Wang, H.-Y., Zhou, A.-M., & Qi, J.-Y. (2022) · Entropy · doi:10.3390/e24071010 · Cited by 21 · see citing works
Adaptive Domain-Aware Representation Learning for Speech Emotion Recognition
Fan, W., Xu, X., Xing, X., & Huang, D. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-2572 · Cited by 14 · see citing works
Towards Attention-based Contrastive Learning for Audio Spoof Detection
Goel, C., Koppisetti, S., Colman, B., Shahriyari, A., & Bharaj, G. (2023) · INTERSPEECH 2023 · doi:10.21437/interspeech.2023-245 · Cited by 7 · see citing works
- Authors
- Satt, A., Rozenberg, S., & Hoory, R.
- ORCID
- Hoory, Ron
- Venue
- Interspeech 2017
- Publisher
- ISCA
- Published
- 2017-08-20
- Pages
- 1089-1093
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2747664154
- Cited by
- 422
- References
- 12
- Citations by year
- 2026: 17 · 2025: 39 · 2024: 45 · 2023: 59 · 2022: 62 · 2021: 90 · 2020: 53 · 2019: 43 · 2018: 14
- FWCI
- 34.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- emotion recognition, speech emotion recognition, spectrogram, deep learning, audio features
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