Multimodal (Ml)
Multimodal machine learning integrates various data modalities—such as text, images, audio, and video—to create models that mirror human sensory perception. By processing and correlating information across these modalities, these models achieve a holistic data understanding, leading to enhanced accuracy and robustness in tasks like speech recognition, image captioning, sentiment analysis, and biometric identification.
Areas of application
- Human-Computer Interaction
- Healthcare and Biometrics
- Autonomous Vehicles
- Virtual and Augmented Reality
- Linguistics and Text Processing
- Video Surveillance
- Robotics
- Sentiment Analysis
Example
A multimodal ML model could be trained on a dataset that includes both text and audio inputs. The model would learn to recognize patterns in both the spoken words and the accompanying emotions conveyed through tone of voice, leading to improved speech recognition accuracy.