Chiranjeevi Yarra
Chiranjeevi Yarra[edit | edit source]
Dr. Chiranjeevi Yarra has joined Language Technologies Research Center (LTRC) IIIT-H – Speech Processing Lab as Assistant Professor in 2020. He has been awarded with Prof. D J Badkas Medal for the academic year 2019 – 2020 by the council of the institute for his best performance in Ph. D at IISc Bangalore during the online convocation held on 6 October.[1][2]
Education[edit | edit source]
Prof Chiranheevi's academic journey began at the National Institute of Technology, Warangal, where a Bachelor of Technology (BTech) in Electrical and Electronics Engineering was completed between 2002 and 2006. This was followed by postgraduate studies at the Indian Institute of Technology, Kharagpur, culminating in a Master of Technology (MTech) in Instrumentation and Signal Processing from 2007 to 2009. Further advancing academic pursuits, a Doctor of Philosophy (PhD) in Speech Signal Processing was undertaken at the Indian Institute of Science (IISc), Bangalore, spanning the years 2013 to 2020.[3]
Research & Publications[edit | edit source]
His research interests are Speech Signal Processing, Machine Learning, Digital Signal Processing and Time-varying Signal Analysis.[4]
- Exploring the Use of Self-Supervised Representations for Automatic Syllable Stress Detection in 2024.[5]
- A comparative analysis of sequential models that integrate syllable dependency for automatic syllable stress detection in 2024.[6]
- Study of Indian English pronunciation variabilities relative to Received Pronunciation in 2023.[7]
- IIITH MM2 Speech-Text: A preliminary data for automatic spoken data validation with matched and mismatched speech-text content in 2023.[8]
- Automatic syllable stress detection under non-parallel label and data condition in 2022.[9]
- A study on native American English speech recognition by Indian listeners with varying word familiarity level in 2021.[10]
- Pronunciation assessment and semi-supervised feedback prediction for spoken English tutoring in 2020.[11]
- Low Resource Automatic Intonation Classification Using Gated Recurrent Unit (GRU) Networks Pre-Trained with Synthesized Pitch Patterns in 2019.[12]
- Automatic native language identification using novel acoustic and prosodic feature selection strategies in 2018.[13]
- A comparative study on the effect of different codecs on speech recognition accuracy using various acoustic modeling techniques in 2017.[14]
References[edit | edit source]
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