Niko Brümmer

3.2k total citations · 1 hit paper
34 papers, 1.8k citations indexed

About

Niko Brümmer is a scholar working on Artificial Intelligence, Signal Processing and Control and Systems Engineering. According to data from OpenAlex, Niko Brümmer has authored 34 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Artificial Intelligence, 26 papers in Signal Processing and 1 paper in Control and Systems Engineering. Recurrent topics in Niko Brümmer's work include Speech Recognition and Synthesis (30 papers), Speech and Audio Processing (25 papers) and Music and Audio Processing (22 papers). Niko Brümmer is often cited by papers focused on Speech Recognition and Synthesis (30 papers), Speech and Audio Processing (25 papers) and Music and Audio Processing (22 papers). Niko Brümmer collaborates with scholars based in Czechia, South Africa and Italy. Niko Brümmer's co-authors include Johan A. du Preez, David A. van Leeuwen, Lukáš Burget, Patrick Kenny, Ondřej Glembek, Edward de Villiers, Najim Dehak, Pierre Dumouchel, Pavel Matějka and Sandro Cumani and has published in prestigious journals such as IEEE Transactions on Audio Speech and Language Processing, Computer Speech & Language and Radboud Repository (Radboud University).

In The Last Decade

Niko Brümmer

34 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

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.

Application-independent evaluation of speaker detection

2005 427 citations Niko Brümmer et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Niko Brümmer Czechia	18	1.7k	1.5k	189	63	42	34	1.8k
Ondřej Glembek Czechia	23	2.1k 1.2×	1.8k 1.2×	138 0.7×	79 1.3×	6 0.1×	57	2.2k
Terri Kamm United States	7	777 0.5×	693 0.5×	256 1.4×	63 1.0×	12 0.3×	8	1.1k
Pavel Matějka Czechia	26	2.0k 1.2×	1.9k 1.3×	259 1.4×	103 1.6×	7 0.2×	63	2.3k
Petr Schwarz Czechia	16	1.3k 0.8×	1.1k 0.7×	112 0.6×	72 1.1×	6 0.1×	27	1.4k
Sylvain Meignier France	14	949 0.6×	871 0.6×	183 1.0×	36 0.6×	3 0.1×	39	1.1k
Hagai Aronowitz Israel	16	612 0.4×	610 0.4×	200 1.1×	96 1.5×	2 0.0×	51	837
Themos Stafylakis Greece	18	1.0k 0.6×	985 0.7×	275 1.5×	20 0.3×	2 0.0×	60	1.3k
Sachin Kajarekar United States	20	1.3k 0.8×	1.2k 0.8×	119 0.6×	84 1.3×	2 0.0×	52	1.4k
Jason Pelecanos United States	12	833 0.5×	836 0.6×	57 0.3×	27 0.4×	4 0.1×	43	929
Pegah Ghahremani United States	12	1.1k 0.7×	901 0.6×	65 0.3×	136 2.2×	4 0.1×	18	1.3k

Countries citing papers authored by Niko Brümmer

Since Specialization

Citations

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

Fields of papers citing papers by Niko Brümmer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Niko Brümmer

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Stafylakis, Themos, Ladislav Mošner, Oldřich Plchot, et al.. (2022). Analyzing Speaker Verification Embedding Extractors and Back-Ends Under Language and Channel Mismatch. 4 indexed citations

2.

Yılmaz, Emre, Kun Zhou, Henk van den Heuvel, et al.. (2019). \nLarge-Scale Speaker Diarization of Radio Broadcast Archives. Radboud Repository (Radboud University). 2 indexed citations

3.

Villalba, Jesús, Niko Brümmer, & Najim Dehak. (2018). End-to-End versus Embedding Neural Networks for Language Recognition in Mismatched Conditions. 112–119. 15 indexed citations

4.

Villalba, Jesús, Niko Brümmer, & Najim Dehak. (2017). Tied Variational Autoencoder Backends for i-Vector Speaker Recognition. 1004–1008. 13 indexed citations

5.

Plchot, Oldřich, Pavel Matějka, Ondřej Glembek, et al.. (2016). BAT System Description for NIST LRE 2015. 166–173. 9 indexed citations

6.

Lee, Kong Aik, Anthony Larcher, Guangsen Wang, et al.. (2015). The reddots data collection for speaker recognition. 99 indexed citations

7.

Brümmer, Niko, Alan McCree, Stephen Shum, Daniel Garcia-Romero, & Carlos Vaquero. (2014). Unsupervised Domain Adaptation for I-Vector Speaker Recognition. 260–264. 79 indexed citations

8.

Leeuwen, David A. van & Niko Brümmer. (2013). The distribution of calibrated likelihood-ratios in speaker recognition. Radboud Repository (Radboud University). 41 indexed citations

9.

Plchot, Oldřich, Martin Karafiát, Niko Brümmer, et al.. (2012). Speaker vectors from subspace Gaussian mixture model as complementary features for language identification.. 330–333. 4 indexed citations

10.

Cumani, Sandro, Ondřej Glembek, Niko Brümmer, Edward de Villiers, & Pietro Laface. (2012). Gender independent discriminative speaker recognition in i-vector space. 4361–4364. 16 indexed citations

11.

Burget, Lukáš, Oldřich Plchot, Sandro Cumani, et al.. (2011). Discriminatively trained Probabilistic Linear Discriminant Analysis for speaker verification. 4832–4835. 110 indexed citations

12.

Leeuwen, David A. van & Niko Brümmer. (2011). A speaker line-up for the likelihood ratio. Radboud Repository (Radboud University). 501–504. 4 indexed citations

13.

Villalba, Jesús & Niko Brümmer. (2011). Towards fully Bayesian speaker recognition: integrating out the between-speaker covariance. 505–508. 41 indexed citations

14.

Brümmer, Niko & Edward de Villiers. (2010). The speaker partitioning problem. 34. 95 indexed citations

15.

Plchot, Oldřich, Niko Brümmer, Lukáš Burget, et al.. (2010). Data selection and calibration issues in automatic language recognition - investigation with BUT-AGNITIO NIST LRE 2009 system.. 37. 21 indexed citations

16.

Dehak, Najim, Réda Dehak, Patrick Kenny, et al.. (2009). Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification. 1559–1562. 228 indexed citations

17.

Glembek, Ondřej, Lukáš Burget, Najim Dehak, Niko Brümmer, & Patrick Kenny. (2009). Comparison of scoring methods used in speaker recognition with Joint Factor Analysis. 4057–4060. 91 indexed citations

18.

Leeuwen, David A. van & Niko Brümmer. (2008). Building language detectors using small amounts of training data. 15. 1 indexed citations

19.

Brümmer, Niko & David A. van Leeuwen. (2006). On calibration of language recognition scores. 1–8. 86 indexed citations

20.

Leeuwen, David A. van & Niko Brümmer. (2006). Channel-dependent GMM and Multi-class Logistic Regression models for language recognition. 2. 1–8. 27 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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