Editing Chiranjeevi Yarra

{{Infobox Professor
| education = Ph.D
| occupation = Professor
| Institute = IIIT-H
| specialization =  Speech Signal Processing and Machine Learning
}}

==Chiranjeevi Yarra ==
'''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.<ref>{{Cite web |title=Faculty profile IIIT hyderabad |url=https://www.iiit.ac.in/faculty/chiranjeevi-yarra/}}</ref><ref>{{Cite web |title=Awards prof |url=https://blogs.iiit.ac.in/monthly_news/chiranjeevi-yarra-awarded-prof-d-j-badkas-medal/}}</ref>

== Education ==
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.<ref>{{Cite web |title=Education details Chiranjeevi yarra |url=https://blogs.iiit.ac.in/monthly_news/chiranjeevi-yarra/}}</ref>

==Research & Publications==
His research interests are Speech Signal Processing, Machine Learning, Digital Signal Processing and Time-varying Signal Analysis.<ref>{{Cite web |title=IEEE profile page |url=https://ieeexplore.ieee.org/author/37085781699}}</ref>

# Exploring the Use of Self-Supervised Representations for Automatic Syllable Stress Detection in 2024.<ref>{{Cite web |title=Exploring the Use of Self-Supervised Representations for Automatic Syllable Stress Detection |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:HDshCWvjkbEC}}</ref>
# A comparative analysis of sequential models that integrate syllable dependency for automatic syllable stress detection in 2024.<ref>{{Cite web |title=A comparative analysis of sequential models that integrate syllable dependency for automatic syllable stress detection |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:e5wmG9Sq2KIC}}</ref>
# Study of Indian English pronunciation variabilities relative to Received Pronunciation in 2023.<ref>{{Cite web |title=Study of Indian English pronunciation variabilities relative to Received Pronunciation |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:QIV2ME_5wuYC}}</ref>
# IIITH MM2 Speech-Text: A preliminary data for automatic spoken data validation with matched and mismatched speech-text content in 2023.<ref>{{Cite web |title=IIITH MM2 Speech-Text: A preliminary data for automatic spoken data validation with matched and mismatched speech-text content |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:mB3voiENLucC}}</ref>
# Automatic syllable stress detection under non-parallel label and data condition in 2022.<ref>{{Cite web |title=Automatic syllable stress detection under non-parallel label and data condition |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:9ZlFYXVOiuMC}}</ref>
# A study on native American English speech recognition by Indian listeners with varying word familiarity level in 2021.<ref>{{Cite web |title=A study on native American English speech recognition by Indian listeners with varying word familiarity level |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:mVmsd5A6BfQC}}</ref>
# Pronunciation assessment and semi-supervised feedback prediction for spoken English tutoring in 2020.<ref>{{Cite web |title=Pronunciation assessment and semi-supervised feedback prediction for spoken English tutoring |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:Zph67rFs4hoC}}</ref>
# Low Resource Automatic Intonation Classification Using Gated Recurrent Unit (GRU) Networks Pre-Trained with Synthesized Pitch Patterns in 2019.<ref>{{Cite web |title=Low Resource Automatic Intonation Classification Using Gated Recurrent Unit (GRU) Networks Pre-Trained with Synthesized Pitch Patterns. |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:Se3iqnhoufwC}}</ref>
# Automatic native language identification using novel acoustic and prosodic feature selection strategies in 2018.<ref>{{Cite web |title=Automatic native language identification using novel acoustic and prosodic feature selection strategies |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:eQOLeE2rZwMC}}</ref>
# A comparative study on the effect of different codecs on speech recognition accuracy using various acoustic modeling techniques in 2017.<ref>{{Cite web |title=A comparative study on the effect of different codecs on speech recognition accuracy using various acoustic modeling techniques |url=https://scholar.google.com/citations?view_op=view_citation&hl=en&user=w1h4kYEAAAAJ&cstart=20&pagesize=80&citation_for_view=w1h4kYEAAAAJ:2osOgNQ5qMEC}}</ref>



== References ==

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