Papers by Kamer Ali Yuksel
An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation (2024.lrec-main)
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Ahmet Gunduz, Kamer Ali Yuksel, Kareem Darwish, Golara Javadi, Fabio Minazzi, Nicola Sobieski, Sébastien Bratières
| Challenge: | Text-to-speech (TTS) models require data availability and quality of training data. |
| Approach: | They propose an end-to-end tool to generate high-quality datasets for text-to speech models . language-specific phoneme distribution is integrated into sample selection, they argue . |
| Outcome: | The proposed tool aims to streamline the dataset creation process for voice-based technologies by integrating language-specific phonemes into sample selection and quality assurance of recordings. |
Agentic AI for Human Resources: LLM-Driven Candidate Assessment (2026.eacl-demo)
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Kamer Ali Yuksel, Abdul Basit Anees, Ashraf Hatim Elneima, Sanjika Hewavitharana, Mohamed Al-Badrashiny, Hassan Sawaf
| Challenge: | Current systems rely on keyword matching and shallow keyword-based screening, leading to missed opportunities and inconsistent evaluations. |
| Approach: | They propose a framework that uses Large Language Models to automate candidate assessment in recruitment. |
| Outcome: | The proposed framework outputs detailed assessment reports, candidate comparisons, and ranked recommendations that are transparent, auditable, and suitable for real-world hiring workflows. |
MTLens: Machine Translation Output Debugging (2022.lrec-1)
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Shreyas Sharma, Kareem Darwish, Lucas Pavanelli, Thiago Castro Ferreira, Mohamed Al-Badrashiny, Kamer Ali Yuksel, Hassan Sawaf
| Challenge: | a demo demonstrates a system for quantitatively evaluating MT systems in isolation or multiple MT models collectively . performance of machine translation systems varies significantly with inputs of diverging features, such as genres, genres and surface properties. |
| Approach: | They propose a benchmarking interface that quantitatively evaluates MT systems in isolation or collectively . the interface can be extended to include additional filters such as lexical, morphological, and syntactic features. |
| Outcome: | The proposed system quantitatively evaluates MT systems on multiple domains and evaluation metrics. |