Rashmi Nagpal
Rashmi is a AI Research Scientist at Poseidon and a researcher at MIT CSAIL, working in the intersection of cybersecurity and artificial intelligence. She has six years of industrial experience, having brought ideas to life at pre-seed startups and contributed to impactful redesigns and features at established industry giants. Beyond coding, Rashmi finds inspiration in capturing the wonders of the cosmos through her telescope and engaging in board games with friends.
Session
Building machine learning models for audio deepfake detection seems straightforward until datasets span multiple languages, such as Hindi, Korean, Mandarin, and German. In practice, multilingual Automatic Speech Recognition (ASR) systems often fail in production because language-specific acoustic variations and assumptions about the processing pipeline break down at scale.
This talk examines the engineering challenges of building a multilingual deepfake detection system using a Python-centric pipeline. It covers practical issues encountered during large-scale audio preprocessing, including memory-efficient data loading, resumable feature-extraction workflows, and validation strategies designed to prevent cross-lingual leakage. The session also shares lessons from deploying a multilingual ASR-based system, with a focus on pipeline structure, evaluation correctness, and operational robustness in real-world settings.