Sundeep Teki
  • Home
  • Build
  • Consult
  • Research
    • Publications
    • AI
    • Neuroscience >
      • Speech
      • Time
      • Memory
    • Talks
  • BLOG
  • Contact
    • News
    • Media
ARTIFICIAL INTELLIGENCE

​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
    • 2nd in Microsoft Challenge on Speech Technologies for Code-Mixing; Paper accepted at INTERSPEECH 2020
  • 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
COPYRIGHT © 2021,  SUNDEEP TEKI
  • Home
  • Build
  • Consult
  • Research
    • Publications
    • AI
    • Neuroscience >
      • Speech
      • Time
      • Memory
    • Talks
  • BLOG
  • Contact
    • News
    • Media