Keywords:
- BoAW
- breathing pattern estimation
- breathing patterns
- ComParE features
- Convolution Neural Network
- Convolutional Neural Networks
- COVID-19 identification
- depression detection
- Emotion Recognition
- end-to-end modelling
- Expressive Vocalizations
- feature selection
- fine-tuning
- Fusion
- glottal source signals.
- modalities fusion
- Multi-task learning
- multiple linear regression
- phoneme modeling
- Phoneme recognition
- Phonetic information
- pre-trained embedding
- raw-waveform cnn
- Self-supervised embedding
- self-supervised learning
- Speech Emotion Recognition
- zero-frequency filtering
Publications of Bogdan Vlasenko sorted by journal and type
Publications of type Idiap-RR
2018
| Modelling glottal source information for depression detection, , and , Idiap-RR-13-2018 |
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Interspeech (2023)
| Implicit phonetic information modeling for speech emotion recognition, , and , in: Interspeech, Dublin, Ireland, ISCA, 2023 |
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2023)
| Towards learning emotion information from short segments of speech, , , , and , in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Rhodes Island, Greece, IEEE, 2023 |
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International Multimodal Sentiment Analysis Workshop and Challenge (2022)
| Comparing Biosignal and Acoustic feature Representation for Continuous Emotion Recognition, , , , and , in: International Multimodal Sentiment Analysis Workshop and Challenge, 2022 |
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Proceedings of the ICML Expressive Vocalizations Workshop held in conjunction with the 39th International Conference on Machine Learning (2022)
| Comparing supervised and self-supervised embedding for ExVo Multi-Task learning track, , , and , in: Proceedings of the ICML Expressive Vocalizations Workshop held in conjunction with the 39th International Conference on Machine Learning, Maryland, USA, 2022 |
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Proceedings of ICASSP (2022)
| Modeling Of Pre-trained Neural Network Embeddings Learned From Raw Waveform For Covid-19 Infection Detection, , , and , in: Proceedings of ICASSP, 2022 |
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2nd Multimodal Sentiment Analysis Challenge (MuSe '21), October 24, 2021, Virtual Event, China (2021)
| Fusion of Acoustic and Linguistic Information Using Supervised Autoencoder for Improved Emotion Recognition, , and , in: 2nd Multimodal Sentiment Analysis Challenge (MuSe '21), October 24, 2021, Virtual Event, China, 2021 |
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2019)
| Learning voice source related information for depression detection, , and , in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019 |
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Proceedings of Interspeech 2018 (2018)
| Implementing Fusion Techniques for the Classification of Paralinguistic Information, , , and , in: Proceedings of Interspeech 2018, pages 526-530, 2018 |
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Proceedings of Interspeech 2017 (2017)
| Implementing gender-dependent vowel-level analysis for boosting speech-based depression recognition, , , and , in: Proceedings of Interspeech 2017, 2017 |
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Proceedings of Interspeech (2015)
| Annotators' agreement and spontaneous emotion classification performance, and , in: Proceedings of Interspeech, pages 1546-1550, 2015 |
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Proceedings of ACII (2015)
| Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies (Extended Abstract), , , , , , and , in: Proceedings of ACII, Xi'an, pages 470-476, IEEE, 2015 |
[DOI] |
| Exploring Dataset Similarities using PCA-based Feature Selection, , , and , in: Proceedings of ACII, Xi'an, pages 387-393, IEEE, 2015 |
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6th IEEE Conference on Cognitive Infocommunications (2015)
| Overlapping Speech, Utterance Duration and Affective Content in HHI and HCI - an Comparison, , , , and , in: 6th IEEE Conference on Cognitive Infocommunications, Gyor, pages 83-88, 2015 |
[DOI] |
International Conference on Acoustics, Speech and Signal Processing (2024)
| COMPARING DATA-DRIVEN AND HANDCRAFTED FEATURES FOR DIMENSIONAL EMOTION RECOGNITION, , and , in: International Conference on Acoustics, Speech and Signal Processing, 2024 |
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