Core Features:

• Multilingual speech-to-text across diverse accents and dialects
• Emotion and sentiment detection from vocal patterns and prosody
• Real-time and batch transcription pipelines
• High-accuracy inference via model optimization and fine-tuning
• Continuous learning with feedback/evaluation loops

AI Integration:

• Develop an AI-driven voice transcription and emotional analysis engine that processes audio input and outputs accurate, language-aware transcripts enriched with emotional context. Mobile integration (e.g., React Native) is secondary and a thin client.

Tech Stack:

• AI/ML: TensorFlow, PyTorch
• Speech/NLP: Speech-to-Text, NLP Pipelines, Signal Processing
• Backend/Data: Python, Model APIs, Data Pipelines

Profile:

• Engineer or Bachelor degree in Computer Science