Elamur Research⌕
☰
home / authors / Daniel Korzekwa

Daniel Korzekwa

Daniel Korzekwa has 4 papers in our library, in 2020–2022, most of them on English; often with Bozena Kostek, Jaime Lorenzo-Trueba, Thomas Drugman.

4 papers in our library · 2020–2022

OpenAlex · ORCID · h-index 10 · 25 works · 264 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 11 citations in our library · ⭐ Paper Star rank #5323

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢AmazonSeattle · United States2018–2023 · 14 papers🎓Gdańsk University of TechnologyGdansk · Poland2020–2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 7 Arts and Humanities · 4 works Computer Science · 4 works Psychology · 4 works Artificial Intelligence · 4 works Experimental and Cognitive Psychology · 4 works Language and Linguistics · 4 works Signal Processing · 4 works

🧭 Topics 1 English · 4 works

 Works by year

2020s

20221 paper20212 papers20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling (2021 · 27 citations)
    Computer Assisted Pronunciation Training (CAPT) systems usually transcribe a learner's phonemes and flag any mismatch with one canonical native pronunciation.
  2. Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention (2020 · 16 citations)
    Pronunciation-training software usually drills individual sounds and seldom checks whether a learner stresses the right syllable, although misplaced stress makes L2 English harder to understand.
  3. Weakly-supervised word-level pronunciation error detection in non-native English speech (2021 · 15 citations)
    The paper addresses automatic detection of mispronounced words in learner (L2) English. Most prior systems need L2 recordings that humans have transcribed phoneme by phoneme, which is slow, costly and sometimes ill-defined. The authors propose WEAKLY-S, which needs only a flag on each word saying whether it was…
  4. Automated detection of pronunciation errors in non-native English speech employing deep learning (2022 · 1 citation)
    This doctoral dissertation, an "Implementation Doctorate" run jointly by Gdańsk University of Technology and Amazon TTS Research, addresses automatic pronunciation assessment inside Computer-Assisted Pronunciation Training (CAPT) for non-native (L2) English learners.

 Co-authors here

5 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 11 times (of the 141,840 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/daniel-korzekwa/