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