arXiv (All)
open access
Leveraging Unlabeled Audio-Visual Data in Speech Emotion Recognition using Knowledge Distillation
audio-and-speech-processinghuman-computer-interactionimage-and-video-processingmachine-learningmultimediasignal-processing
Voice interfaces integral to the human-computer interaction systems can benefit from speech emotion recognition (SER) to customize responses based on user emotions. Since humans convey emotions through multi-modal audio-visual cues, developing SER systems using both the modalities is beneficial. How
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A smart space with music selection feature based on face and speech emotion and expression recognition#261010
- Enriching Speech Emotion Representations with Conversational Context#1062316
- Consensus-Guided Shared-Specific Tri-View Learning for Speech Emotion Recognition#1035648
- Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations#1063248
- Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition#1033859
Record · ID 974203
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive