Multimodal fusion: A study on speech-text emotion recognition with the integration of deep learning

Multimodal fusion: A study on speech-text emotion recognition with the integration of deep learning

Recognition of various human emotions holds significant value in numerous real-world scenarios. This paper focuses on the multimodal fusion of speech and text for emotion recognition. A 39-dimensional Mel-frequency cepstral coefficient (MFCC) was used as a feature for speech emotion. A 300-dimension...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy:	Yanan Shang, Tianqi Fu
Format:	Artykuł
Język:	English
Wydane:	Elsevier 2024-12-01
Seria:	Intelligent Systems with Applications
Hasła przedmiotowe:	Multimodal fusion Deep learning Glove model BiGRU Emotion recognition
Dostęp online:	http://www.sciencedirect.com/science/article/pii/S2667305324001108

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