Emily Mower Provost
- Experimental and Cognitive Psychology top 1%
- Artificial Intelligence top 2%
- Signal Processing top 1%
- Computer Vision and Pattern Recognition top 5%
- Cognitive Neuroscience top 10%
- Co-authors
- Yelin KimDuc LeHonglak LeeMelvin G. McInnisCarlos BussoGeorg EsslNajmeh SadoughiSrinivas Parthasarathy
- Topics
- Emotion and Mood Recognition (28 papers)Speech and Audio Processing (25 papers)Music and Audio Processing (20 papers)
- Partner nations
- United StatesNetherlandsCanada
In The Last Decade
Emily Mower Provost
69 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 106
- Experimental and Cognitive Psychology 1.1k
- Artificial Intelligence 775
- Signal Processing 722
- Computer Vision and Pattern Recognition 326
- Cognitive Neuroscience 250
Countries citing papers authored by Emily Mower Provost
This map shows the geographic impact of Emily Mower Provost'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 Emily Mower Provost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emily Mower Provost more than expected).
Fields of papers citing papers by Emily Mower Provost
This network shows the impact of papers produced by Emily Mower Provost. 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 Emily Mower Provost. The network helps show where Emily Mower Provost may publish in the future.
Co-authorship network of co-authors of Emily Mower Provost
This figure shows the co-authorship network connecting the top 25 collaborators of Emily Mower Provost. A scholar is included among the top collaborators of Emily Mower Provost based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Emily Mower Provost. Emily Mower Provost is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 6 | |
| 9 | 10 | |
| 10 | MuSE: a Multimodal Dataset of Stressed Emotion | 11 |
| 11 | 4 | |
| 12 | 11 | |
| 13 | 29 | |
| 14 | 16 | |
| 15 | 50 | |
| 16 | 2 | |
| 17 | 34 | |
| 18 | 55 | |
| 19 | 99 | |
| 20 | Simplifying emotion classification through emotion distillation | 9 |
About Emily Mower Provost
Emily Mower Provost is a scholar working on Signal Processing, Experimental and Cognitive Psychology and Artificial Intelligence, having authored 73 papers that have together received 1.8k indexed citations. Recurring topics across this work include Emotion and Mood Recognition (28 papers), Speech and Audio Processing (25 papers) and Music and Audio Processing (20 papers). The work is most often cited by research in Experimental and Cognitive Psychology (1.1k citations), Signal Processing (722 citations) and Artificial Intelligence (775 citations). Emily Mower Provost has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include Yelin Kim, Duc Le, Honglak Lee, Melvin G. McInnis, Carlos Busso, Georg Essl, Najmeh Sadoughi, Srinivas Parthasarathy, Mohammed Abdelwahab and Carol Persad. Their work appears in journals such as PLoS ONE, Proceedings of the IEEE and Scientific Reports.
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.