Papers by Ahmet Gunduz

2 papers
EvolveMT: an Ensemble MT Engine Improving Itself with Usage Only (2023.acl-industry)

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Challenge: EvolveMT is a method for the efficient combination of multiple machine translation engines.
Approach: They propose a method that selects the output from one engine for each segment and uses online learning techniques to predict the most appropriate system for each translation request.
Outcome: The proposed method achieves similar translation accuracy at a lower cost than selecting the best translation of each segment from all translations using an MT quality estimator.
An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation (2024.lrec-main)

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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.

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