Abhinav G.

I am leading Flipkart's Speech Recognition to build ASR for various use cases, domains, and languages. As a part of this role, I have built in-house ASR models for Indian languages which power Flipkar's Voice Search. Currently, I am working towards building more robust and generic ASR models for Indian E-commerce. I am also a core contributor to the Large Language Models team. As a part of this, I am building in-house LLMs for various NLP use cases like generating product descriptions and end-to-end shopping assistants. In the past, I've also worked on augmenting the in-house translation models to infer word alignments, constituency parsing, and NLU tag transfer. I've also implemented solutions for entity classification and grapheme-to-phoneme conversion.

Developed Machine Reading system to answer comprehension based factual questions using Tesseract-OCR and pre-trained RNN models. Used Stanford-NLP for extracting entities and relations from comprehension. Trained CNN models in keras using supervised learning and transfer learning to recognize emotions from face images. Used opencv to detect faces and trained DNNs to recognize emotions from facial landmarks. Automated the detection and classification of defects in TV Video Stream by using statistical anomaly detection methods like moving average and rolling standard deviation.