Advanced text-to-speech system that uses visual cues to generate natural prosody and emotional expression in synthesized speech, creating more human-like and contextually appropriate voice output.

Overview

This project explores the intersection of computer vision and speech synthesis, leveraging visual information to enhance the naturalness and emotional expressiveness of text-to-speech systems. By analyzing visual cues from images or video, the system generates more contextually appropriate prosody and emotional intonation in synthesized speech.

Key Features

• Visual cue extraction and analysis
• Prosody modeling based on visual context
• Emotional expression synthesis
• Context-aware TTS generation
• Integration of multimodal AI approaches

Technologies

• Python
• TTS (Text-to-Speech)
• Computer Vision
• Deep Learning
• Prosody Control

Research Focus

The project addresses the challenge of generating emotionally expressive and contextually appropriate speech by incorporating visual information. This multimodal approach enables the TTS system to understand the emotional context and adjust prosodic features accordingly, resulting in more natural and engaging synthesized speech.