ARTIFICIAL INTELLIGENCE
My work is focused on applications of deep learning for improving virtual assistant AI using:
My work is focused on applications of deep learning for improving virtual assistant AI using:
- End-to-end speech recognition
- Natural language understanding for Chatbot
- Neural machine translation
- Sequence models
- Distributed deep learning
- Speech recognition for low-resource, code-mixed languages
- Sentiment analysis in text and voice
End-to-end Speech Recognition for Virtual Assistants
- Developed state-of-the-art end-to-end deep learning ASR models based on sequence-to-sequence models
- Significantly improved ASR model training times using ring-allreduce distributed training
- Trained ASR models on >20000 hours of data using cloud infrastructure
NLP: Sensitive interactions between Humans and Virtual Assistants
- Trained deep learning NLP models to detect sensitive content in human-assistant interactions
- Deployed NLP models for non-English languages using Neural Machine Translation and Text Generation techniques
Privacy-preserving deep learning
- Researched homomorphic encryption, federated learning and its applications for private machine and deep learning
Speech Recognition for Low-resource, Code-Mixed Indian Languages
- ASR modeling for "Hinglish", i.e. Hindi interspersed with English common amongst Indian speakers
- Spoken Language Identification for low-resource Indian languages code-mixed with English
- Speaker Diarization modeling, i.e. "who spoke when" for code-mixed speech
- Audio Sentiment Analysis to understand emotions expressed in speech
Applied Natural Language Understanding
- NLU modeling using weak supervision to decode intent in code-mixed chat
- Sentiment analysis to identify negative feelings, emotions and opinions in chat
- Language identification in code-mixed chat
- Predicting Social Media Escalations based on chat input
- Classification of Products into multiple categories based on text inputs
- Named Entity Recognition of various entities in chat conversations