Joel Pinto

501 total citations
24 papers, 344 citations indexed

About

Joel Pinto is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Joel Pinto has authored 24 papers receiving a total of 344 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 19 papers in Signal Processing and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Joel Pinto's work include Speech and Audio Processing (18 papers), Speech Recognition and Synthesis (18 papers) and Music and Audio Processing (10 papers). Joel Pinto is often cited by papers focused on Speech and Audio Processing (18 papers), Speech Recognition and Synthesis (18 papers) and Music and Audio Processing (10 papers). Joel Pinto collaborates with scholars based in Switzerland, Germany and India. Joel Pinto's co-authors include Hynek Heřmanský, Mathew Magimai.-Doss, Hervé Bourlard, Daniel Willett, B. Yegnanarayana, Ralf Schlüter, Zoltán Tüske, S. R. Mahadeva Prasanna, Sharmistha Gray and Igor Szöke and has published in prestigious journals such as IEEE Transactions on Audio Speech and Language Processing, IEEE Transactions on Consumer Electronics and Porto Biomedical Journal.

In The Last Decade

Joel Pinto

21 papers receiving 283 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joel Pinto Switzerland 11 306 240 27 23 17 24 344
Josef Psutka Czechia 11 385 1.3× 187 0.8× 34 1.3× 52 2.3× 7 0.4× 54 457
R.A. Sukkar United States 10 273 0.9× 171 0.7× 18 0.7× 54 2.3× 4 0.2× 29 318
Tanel Alumäe Estonia 11 427 1.4× 118 0.5× 19 0.7× 46 2.0× 6 0.4× 49 484
Ravichander Vipperla United Kingdom 11 272 0.9× 224 0.9× 37 1.4× 41 1.8× 4 0.2× 23 348
Djamel Mostefa France 9 343 1.1× 126 0.5× 55 2.0× 41 1.8× 4 0.2× 31 396
Efthymios Tzinis United States 10 145 0.5× 194 0.8× 54 2.0× 28 1.2× 8 0.5× 19 265
Ananth Sankar United States 9 284 0.9× 184 0.8× 7 0.3× 49 2.1× 4 0.2× 25 321
G. Zavaliagkos United States 13 459 1.5× 322 1.3× 54 2.0× 51 2.2× 3 0.2× 31 487
Po-Han Chi Taiwan 2 439 1.4× 293 1.2× 69 2.6× 42 1.8× 4 0.2× 3 527
Kate Knill United Kingdom 16 682 2.2× 390 1.6× 52 1.9× 30 1.3× 2 0.1× 53 712

Countries citing papers authored by Joel Pinto

Since Specialization
Citations

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

Fields of papers citing papers by Joel Pinto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joel Pinto

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

All Works

20 of 20 papers shown
1.
Grangeia, Ana, et al.. (2023). Copy number variations on chromosome 2: impact on human phenotype, a cross-sectional study. Porto Biomedical Journal. 8(1). e198–e198.
2.
Diehl, Frank, Stefan Hahn, Yue Pan, et al.. (2020). Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models. 22–30. 34 indexed citations
3.
Diehl, Frank, et al.. (2018). Scalable Wide and Deep Learning for Computer Assisted Coding. 1–7. 2 indexed citations
4.
Zhang, Zixing, Joel Pinto, Christian Plahl, Björn W. Schuller, & Daniel Willett. (2014). Channel mapping using bidirectional long short-term memory for dereverberation in hands-free voice controlled devices. IEEE Transactions on Consumer Electronics. 60(3). 525–533. 18 indexed citations
5.
Gray, Sharmistha, et al.. (2014). Child automatic speech recognition for US English: child interaction with living-room-electronic-devices.. 21–26. 28 indexed citations
6.
Tüske, Zoltán, Joel Pinto, Daniel Willett, & Ralf Schlüter. (2013). Investigation on cross- and multilingual MLP features under matched and mismatched acoustical conditions. 39 indexed citations
7.
Magimai.-Doss, Mathew, et al.. (2011). Posterior features for template-based ASR. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 4864–4867. 20 indexed citations
8.
Pinto, Joel, Mathew Magimai.-Doss, & Hervé Bourlard. (2011). Hierarchical tandem features for ASR in Mandarin. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1241–1244. 2 indexed citations
9.
Pinto, Joel, et al.. (2010). Analysis of MLP-Based Hierarchical Phoneme Posterior Probability Estimator. IEEE Transactions on Audio Speech and Language Processing. 19(2). 225–241. 65 indexed citations
10.
Magimai.-Doss, Mathew, et al.. (2009). On MLP-based Posterior Features for Template-based ASR. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2 indexed citations
11.
Pinto, Joel, et al.. (2009). Volterra series for analyzing MLP based phoneme posterior estimator. 1813–1816. 3 indexed citations
12.
Pinto, Joel, B. Yegnanarayana, Hynek Heřmanský, & Mathew Magimai.-Doss. (2008). Exploiting contextual information for improved phoneme recognition. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 4449–4452. 42 indexed citations
13.
Pinto, Joel, Igor Szöke, S. R. Mahadeva Prasanna, & Hynek Heřmanský. (2008). Fast Approximate Spoken Term Detection from Sequence of Phonemes. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 20 indexed citations
14.
Pinto, Joel & Hynek Heřmanský. (2008). Combining evidence from a generative and a discriminative model in phoneme recognition. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2414–2417. 11 indexed citations
15.
Pinto, Joel, et al.. (2008). Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 3 indexed citations
16.
Pinto, Joel, et al.. (2007). Exploiting phoneme similarities in hybrid HMM-ANN keyword spotting. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1817–1820. 10 indexed citations
17.
Pinto, Joel, et al.. (2007). On Confusions in a Phoneme Recognizer. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 8 indexed citations
18.
Prasanna, S. R. Mahadeva, B. Yegnanarayana, Joel Pinto, & Hynek Heřmanský. (2007). Analysis of Confusion Matrix to Combine Evidence for Phoneme Recognition. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 5 indexed citations
19.
Pinto, Joel, Hervé Bourlard, Zacharie De Grève, & Hynek Heřmanský. (2007). Comparing Different Word Lattice Rescoring Approaches Towards Keyword Spotting. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1 indexed citations
20.
Pinto, Joel, et al.. (2005). Confidence measures in speech recognition based on probability distribution of likelihoods. 3001–3004. 2 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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