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MelTrans: Mel-Spectrogram Relationship-Learning for Speech Emotion Recognition via Transformers.
Li, Hui; Li, Jiawen; Liu, Hai; Liu, Tingting; Chen, Qiang; You, Xinge.
Afiliación
  • Li H; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Li J; National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China.
  • Liu H; National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China.
  • Liu T; National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China.
  • Chen Q; National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China.
  • You X; National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China.
Sensors (Basel) ; 24(17)2024 Aug 25.
Article en En | MEDLINE | ID: mdl-39275417
ABSTRACT
Speech emotion recognition (SER) is not only a ubiquitous aspect of everyday communication, but also a central focus in the field of human-computer interaction. However, SER faces several challenges, including difficulties in detecting subtle emotional nuances and the complicated task of recognizing speech emotions in noisy environments. To effectively address these challenges, we introduce a Transformer-based model called MelTrans, which is designed to distill critical clues from speech data by learning core features and long-range dependencies. At the heart of our approach is a dual-stream framework. Using the Transformer architecture as its foundation, MelTrans deciphers broad dependencies within speech mel-spectrograms, facilitating a nuanced understanding of emotional cues embedded in speech signals. Comprehensive experimental evaluations on the EmoDB (92.52%) and IEMOCAP (76.54%) datasets demonstrate the effectiveness of MelTrans. These results highlight MelTrans's ability to capture critical cues and long-range dependencies in speech data, setting a new benchmark within the context of these specific datasets. These results highlight the effectiveness of the proposed model in addressing the complex challenges posed by SER tasks.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Habla / Emociones Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Habla / Emociones Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza