Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep neural networks for small footprint text-dependent speaker verification
2014645 citationsEhsan Variani, Xin Lei et al.profile →
Generalized End-to-End Loss for Speaker Verification
2018455 citationsLi Wan, Quan Wang et al.profile →
End-to-end text-dependent speaker verification
2016331 citationsIgnacio López Moreno et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Ignacio López Moreno
Since
Specialization
Citations
This map shows the geographic impact of Ignacio López Moreno'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 Ignacio López Moreno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ignacio López Moreno more than expected).
Fields of papers citing papers by Ignacio López Moreno
This network shows the impact of papers produced by Ignacio López Moreno. 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 Ignacio López Moreno. The network helps show where Ignacio López Moreno may publish in the future.
Co-authorship network of co-authors of Ignacio López Moreno
This figure shows the co-authorship network connecting the top 25 collaborators of Ignacio López Moreno.
A scholar is included among the top collaborators of Ignacio López Moreno 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 Ignacio López Moreno. Ignacio López Moreno is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ye, Jia, Yu Zhang, Ron J. Weiss, et al.. (2018). Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis. Neural Information Processing Systems. 31. 4480–4490.90 indexed citations
13.
Moreno, Ignacio López. (2017). La nueva ruralidad y la nueva gobernanza en México : una propuesta de categorización territorial operativa para los nuevos territorios rurales. Redalyc (Universidad Autónoma del Estado de México). 32(92). 217–239.5 indexed citations
14.
Moreno, Ignacio López & Ivonne Vizcarra Bordi. (2016). El maíz nativo en México : una aproximación crítica desde los estudios rurales.
Variani, Ehsan, Xin Lei, Erik McDermott, Ignacio López Moreno, & Javier Gónzalez-Domínguez. (2014). Deep neural networks for small footprint text-dependent speaker verification. 4052–4056.645 indexed citations breakdown →
17.
Moreno, Ignacio López, et al.. (2014). Las etiquetas de calidad agroalimentarias como herramientas de gobernabilidad y desarrollo territorial: los casos del queso de oveja merina de Grazalema y la carne de cordero de Texel. 2106–2118.1 indexed citations
Moreno, Ignacio López, et al.. (2010). Influence of the North Atlantic Oscillation on the streamflows of the Duero basin (Spain): Spatial variability and response times. EGU General Assembly Conference Abstracts. 2131.1 indexed citations
20.
Toledano, Doroteo T., et al.. (2009). Automatic Language Recognition on Spontaneous Speech: The ATVS-UAM System. Journal of the Audio Engineering Society. 57(10). 788–806.
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.