Dr. Tanuka Bhattacharjee is an Indian researcher specializing in artificial intelligence–driven biomedical signal processing, with a strong focus on speech and physiological signal analysis for neurological and cardiovascular health monitoring. She is currently affiliated with TCS Research & Innovation and has extensive experience developing explainable, non-invasive diagnostic systems using machine learning and deep learning techniques.

Online Profiles
Dr. Bhattacharjee maintains an active Google Scholar profile showcasing her peer-reviewed research outputs, citations, and impact metrics, reflecting sustained contributions to speech processing, healthcare AI, and biomedical signal analysis at national and international levels.

Education
Dr. Bhattacharjee completed her PhD at the Indian Institute of Science (IISc), Bengaluru (2019–2025), with a doctoral thesis titled “Characterization and Enhancement of Dysarthric Speech for Amyotrophic Lateral Sclerosis: A Source-Filter Perspective.” She earned her Master of Engineering degree from Jadavpur University (2015–2017), where she was the university topper across all departments, and completed her Bachelor of Technology in Electronics and Communication Engineering from Techno India, Salt Lake (2011–2015), graduating as department topper.

Research Focus
Her primary research focuses on speech signal processing, machine learning, deep learning, and explainable AI for healthcare applications, particularly targeting dysarthria severity analysis, neurodegenerative disease monitoring, cardiovascular diagnostics, and multimodal physiological signal interpretation.

Experience
Dr. Bhattacharjee has served as a Researcher at TCS Research & Innovation during 2017–2019 and from 2025 to present, where she develops automatic, non-invasive cardiovascular and neurological diagnostic tools using speech, ECG, EEG, and wearable sensor data, emphasizing interpretability and clinical relevance.

Research Timeline
Her research journey began with intelligent algorithms and fuzzy systems (2017–2018), expanded into multimodal physiological signal analysis and stress detection (2018–2020), progressed to neuro-speech disorder analysis and ALS-focused dysarthria research (2021–2024), and currently advances explainable AI, transfer learning, and deep learning models for clinical speech and cardiovascular monitoring (2025–2026).

Research Publications
Dr. Bhattacharjee has authored 27 peer-reviewed publications in reputed journals and conferences including Speech Communication (Elsevier), JASA Express Letters, Swarm and Evolutionary Computation, IEEE ICASSP, Interspeech, IEEE EMBC, PerCom Workshops, CinC, and SPCOM, covering topics such as dysarthria severity classification, transfer learning, speech biomarkers for ALS and Parkinson’s disease, cardiovascular signal analysis, and explainable AI methods.

Research Impact
Her research demonstrates strong academic impact with an h-index of 9, i10-index of 7, and 249 citations, indicating consistent scholarly influence across speech processing, biomedical engineering, and healthcare AI domains.

Innovation & Intellectual Property
Dr. Bhattacharjee is credited with six patents, reflecting her commitment to translating research innovations into practical, scalable solutions for healthcare diagnostics and monitoring systems.

Research Projects & Funding
She has contributed to multiple funded research initiatives supported by the Government of India and industry, including doctoral research under the prestigious Prime Minister’s Research Fellowship (PMRF) and industry-driven healthcare innovation projects at TCS Research & Innovation.

Conference Contributions
Dr. Bhattacharjee is a regular contributor to leading international conferences such as IEEE ICASSP, Interspeech, IEEE EMBC, PerCom Workshops, and CinC, and has received competitive travel grants from IEEE Signal Processing Society and ISCA to present her work globally.

Academic Excellence
Her academic excellence is evidenced by multiple top-rank achievements, including university topper awards at both undergraduate and postgraduate levels, along with competitive national fellowships recognizing academic merit and research potential.

Societal / Industry Contribution
Her work contributes directly to society by enabling early, objective, and accessible diagnosis of neurological and cardiovascular conditions, while her industry role supports the development of real-world AI solutions that bridge clinical research and scalable healthcare technology.

Global Recognition
Dr. Bhattacharjee’s research is internationally recognized through global conference presentations, high-impact journal publications, international collaborations, and competitive fellowships, positioning her as a leading emerging researcher in AI-based healthcare analytics.

Publications 
Bhattacharjee, T., Belur, Y., Nalini, A., & Ghosh, P. K. (2026). Source and filter characteristics based transfer learning for dysarthria severity classification in amyotrophic lateral sclerosis. Speech Communication, Elsevier.
Pandey, A., Bhattacharjee, T., et al. (2026). TVP-UNET: Threshold variance penalty U-net for voice activity detection in dysarthric speech. IEEE ICASSP.
Pandey, C., Dutta Choudhury, A., Bhattacharjee, T., & Sinha, A. (2026). Correlation-weighted KAN attribution for explainable AF detection using single-lead ECG. IEEE PerCom Workshops.
Bhattacharjee, T., Vengalil, S., Belur, Y., Nalini, A., & Ghosh, P. K. (2024). Inter-speaker acoustic differences of sustained vowels at varied dysarthria severities for ALS. JASA Express Letters.
Bhattacharjee, T., et al. (2023–2025). Multiple papers in Interspeech, IEEE ICASSP, IEEE SPCOM, IEEE EMBC, CinC, and Swarm and Evolutionary Computation addressing dysarthria, neurodegenerative speech analysis, physiological signal processing, and AI-driven healthcare systems.