S. Lalitha
Impact in
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- Emotion and Mood Recognition
- Signal Processing top 5%
- Speech and Audio Processing
- Music and Audio Processing
Papers in
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- Emotion and Mood Recognition 21
-
- Speech and Audio Processing 10
- Music and Audio Processing 3
- Co-authors
- Deepa Gupta (9 shared papers)Shikha Tripathi (6 shared papers)Mohammed Zakariah (5 shared papers)Yousef Ajami Alotaibi (5 shared papers)Susmitha Vekkot (3 shared papers)Kamran Shaukat (1 shared paper)R Lavanya (1 shared paper)Deepa Gupta (2 shared papers)
- Journals
- Journal of Intelligent & Fuzzy Systems (5 papers)Applied Acoustics (1 paper)International Journal of Speech Technology (1 paper)Sensors (1 paper)IEEE Access (1 paper)
- Partner nations
- IndiaSaudi ArabiaAustralia
In The Last Decade
S. Lalitha
25 papers receiving 369 citations
Peers
Comparison fields: 5 of 71
- Experimental and Cognitive Psychology 225
- Signal Processing 154
- Pharmacy 29
- Artificial Intelligence 168
- Computer Vision and Pattern Recognition 82
Countries citing papers authored by S. Lalitha
This map shows the geographic impact of S. Lalitha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by S. Lalitha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Lalitha more than expected).
Fields of papers citing papers by S. Lalitha
This network shows the impact of papers produced by S. Lalitha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by S. Lalitha. The network helps show where S. Lalitha may publish in the future.
Co-authors
The 8 scholars most cited alongside S. Lalitha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 70 | |
| 2 | 2022 | 56 | |
| 3 | 2018 | 51 | |
| 4 | 2014 | 36 | |
| 5 | 2020 | 30 | |
| 6 | 2015 | 26 | |
| 7 | 2020 | 21 | |
| 8 | 2016 | 18 | |
| 9 | 2014 | 12 | |
| 10 | 2021 | 9 | |
| 11 | 2021 | 9 | |
| 12 | 2022 | 9 | |
| 13 | 2017 | 8 | |
| 14 | 2023 | 7 | |
| 15 | 2021 | 7 | |
| 16 | 2019 | 6 | |
| 17 | 2015 | 5 | |
| 18 | 2018 | 4 | |
| 19 | 2020 | 3 | |
| 20 | 2021 | 3 |
About S. Lalitha
S. Lalitha is a scholar working on Experimental and Cognitive Psychology, Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition and Pharmacy, having authored 29 papers that have together received 397 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (21 papers), Speech and Audio Processing (10 papers), Speech Recognition and Synthesis (8 papers), Infant Health and Development (7 papers), EEG and Brain-Computer Interfaces (6 papers), Face and Expression Recognition (4 papers), Sentiment Analysis and Opinion Mining (3 papers) and Music and Audio Processing (3 papers). The work is most often cited by research in Experimental and Cognitive Psychology (225 citations), Signal Processing (154 citations), Pharmacy (29 citations), Artificial Intelligence (168 citations) and Computer Vision and Pattern Recognition (82 citations). S. Lalitha has collaborated with scholars based in India, Saudi Arabia and Australia. Frequent co-authors include Deepa Gupta, Shikha Tripathi, Mohammed Zakariah, Yousef Ajami Alotaibi, Susmitha Vekkot, Kamran Shaukat, R Lavanya and Deepa Gupta. Their work appears in journals such as Journal of Intelligent & Fuzzy Systems, Applied Acoustics, International Journal of Speech Technology, Sensors and IEEE Access.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.