Subhadeep Dey

464 total citations
20 papers, 320 citations indexed

About

Subhadeep Dey is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Subhadeep Dey has authored 20 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 16 papers in Signal Processing and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Subhadeep Dey's work include Speech Recognition and Synthesis (16 papers), Music and Audio Processing (13 papers) and Speech and Audio Processing (12 papers). Subhadeep Dey is often cited by papers focused on Speech Recognition and Synthesis (16 papers), Music and Audio Processing (13 papers) and Speech and Audio Processing (12 papers). Subhadeep Dey collaborates with scholars based in Switzerland, India and South Korea. Subhadeep Dey's co-authors include Petr Motlíček, Srikanth Madikeri, Seunghyun Yoon, Kyomin Jung, S. R. Mahadeva Prasanna, Rohit Sinha, Rohan Kumar Das, Hervé Bourlard, Lukáš Burget and Hema A. Murthy and has published in prestigious journals such as IEEE Signal Processing Letters, Speech Communication and Infoscience (Ecole Polytechnique Fédérale de Lausanne).

In The Last Decade

Subhadeep Dey

20 papers receiving 289 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Subhadeep Dey Switzerland	10	248	232	78	39	12	20	320
Kyu J. Han United States	12	371 1.5×	311 1.3×	50 0.6×	44 1.1×	11 0.9×	38	456
M. A. Anusuya India	6	214 0.9×	179 0.8×	24 0.3×	39 1.0×	15 1.3×	22	299
Wen-Chin Huang Japan	13	426 1.7×	355 1.5×	51 0.7×	33 0.8×	8 0.7×	38	508
Roxana Mihăescu Romania	6	245 1.0×	217 0.9×	30 0.4×	49 1.3×	6 0.5×	9	302
Piotr Żelasko United States	9	249 1.0×	197 0.8×	25 0.3×	44 1.1×	8 0.7×	33	334
Alexandros Lazaridis Switzerland	8	243 1.0×	227 1.0×	112 1.4×	34 0.9×	10 0.8×	28	334
Bartosz Ziółko Poland	10	128 0.5×	118 0.5×	34 0.4×	65 1.7×	9 0.8×	59	259
Hannah Muckenhirn Switzerland	8	405 1.6×	416 1.8×	26 0.3×	36 0.9×	14 1.2×	11	487
Mengzhe Geng Hong Kong	12	313 1.3×	193 0.8×	122 1.6×	24 0.6×	8 0.7×	37	407
Simon Bozonnet France	7	384 1.5×	394 1.7×	26 0.3×	59 1.5×	10 0.8×	10	490

Countries citing papers authored by Subhadeep Dey

Since Specialization

Citations

This map shows the geographic impact of Subhadeep Dey'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 Subhadeep Dey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Subhadeep Dey more than expected).

Fields of papers citing papers by Subhadeep Dey

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Subhadeep Dey. 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 Subhadeep Dey. The network helps show where Subhadeep Dey may publish in the future.

Co-authorship network of co-authors of Subhadeep Dey

This figure shows the co-authorship network connecting the top 25 collaborators of Subhadeep Dey. A scholar is included among the top collaborators of Subhadeep Dey 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 Subhadeep Dey. Subhadeep Dey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Madikeri, Srikanth, et al.. (2020). Incremental Semi-Supervised Learning for Multi-Genre Speech Recognition. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 7419–7423. 17 indexed citations

Yoon, Seunghyun, et al.. (2019). Speech Emotion Recognition Using Multi-hop Attention Mechanism. arXiv (Cornell University). 2822–2826. 87 indexed citations

Dey, Subhadeep, et al.. (2019). Locating object - of - interest & preventing Stampede using crowd management. 243–247. 1 indexed citations

Madikeri, Srikanth, Petr Motlíček, & Subhadeep Dey. (2019). A Bayesian Approach to Inter-task Fusion for Speaker Recognition. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 5786–5790. 2 indexed citations

Dey, Subhadeep, et al.. (2018). Supermarket Automation with Chatbot and Face Recognition Using IoT and AI. 1183–1186. 8 indexed citations

Dey, Subhadeep, Takafumi Koshinaka, Petr Motlíček, & Srikanth Madikeri. (2018). DNN Based Speaker Embedding Using Content Information for Text-Dependent Speaker Verification. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 5344–5348. 11 indexed citations

Dey, Subhadeep, Srikanth Madikeri, & Petr Motlíček. (2018). End-to-end Text-dependent Speaker Verification Using Novel Distance Measures. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 3598–3602. 7 indexed citations

Madikeri, Srikanth, et al.. (2017). Intra-class covariance adaptation in PLDA back-ends for speaker verification. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 5365–5369. 1 indexed citations

Dey, Subhadeep, et al.. (2017). Template-matching for text-dependent speaker verification. Speech Communication. 88. 96–105. 26 indexed citations

10.

Dey, Subhadeep, et al.. (2017). Exploiting sequence information for text-dependent Speaker Verification. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 5370–5374. 8 indexed citations

11.

Dey, Subhadeep, et al.. (2017). Content Normalization for Text-Dependent Speaker Verification. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1482–1486. 3 indexed citations

12.

Dey, Subhadeep, et al.. (2016). Deep neural network based posteriors for text-dependent speaker verification. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1. 5050–5054. 21 indexed citations

13.

Madikeri, Srikanth, et al.. (2016). IDIAP SUBMISSION TO THE NIST SRE 2016 SPEAKER RECOGNITION EVALUATION. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2 indexed citations

14.

Madikeri, Srikanth, et al.. (2016). Implementation of the Standard I-vector System for the Kaldi Speech Recognition Toolkit. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 9 indexed citations

15.

Dey, Subhadeep, Srikanth Madikeri, & Petr Motlíček. (2016). Information theoretic clustering for unsupervised domain-adaptation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1. 5580–5584. 9 indexed citations

16.

Madikeri, Srikanth, et al.. (2016). A Large-Scale Open-Source Acoustic Simulator for Speaker Recognition. IEEE Signal Processing Letters. 23(4). 527–531. 16 indexed citations

17.

Motlíček, Petr, Subhadeep Dey, Srikanth Madikeri, & Lukáš Burget. (2015). Employment of Subspace Gaussian Mixture Models in speaker recognition. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 20 indexed citations

18.

Dey, Subhadeep, et al.. (2014). Speech biometric based attendance system. 1–6. 62 indexed citations

19.

Dey, Subhadeep & Hema A. Murthy. (2012). Unsupervised Clustering Of Syllables For Language Identification. Zenodo (CERN European Organization for Nuclear Research). 325–329. 1 indexed citations

20.

Dey, Subhadeep, Rajeev Rajan, P.K. Rajan, & Hema A. Murthy. (2011). Feature diversity for emotion, language and speaker verification. 1–5. 9 indexed citations

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.

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