AppTek Achieves Top Ranking at the International Workshop in Spoken Language Translation’s (IWSLT) 2022 Evaluation Campaign

Company’s Spoken Language Translation System Ranks First in Isometric Speech Translation Track Which Is Critical in Improving Automatic Dubbing and Subtitling Workflows

MCLEAN, Va., May 26, 2022 /PRNewswire/ — AppTek, a leader in Artificial Intelligence (AI), Machine Learning (ML), Automatic Speech Recognition (ASR), Neural Machine Translation (NMT), Text-to-Speech (TTS) and Natural Language Processing / Understanding (NLP/U) technologies, announced that its spoken language translation (SLT) system ranked first in the isometric speech translation track at the 19th annual International Workshop on Spoken Language Translation’s (IWSLT 2022) evaluation campaign.

Isometric translation is a new research area in machine translation that concerns the task of generating translations similar in length to the source input and is particularly relevant to downstream applications such as subtitling and automatic dubbing, as well as the translation of some forms of text that require constraints in terms of length such as in software and gaming applications.   

"We are thrilled with the results of the track," said Evgeny Matusov, Lead Science Architect, Machine Translation, at AppTek. "This is a testament to the hard work and skill of our team, who have been focusing on developing customized solutions for the media and entertainment vertical."

AppTek entered the competition to measure the performance of its isometric SLT system against other leading platforms developed by corporate and academic science teams around the world.  Participants were asked to create translations of YouTube video transcriptions such that the length of the translation stays within 10% of the length of the original transcription, measured in terms of characters. AppTek participated in the constrained task for the English-German language pair, which is the one out of the three pairs evaluated at IWSLT with the highest target-to-source length ratio in terms of characters count.

Submissions were evaluated on two dimensions – translation quality and length compliance with respect to source input. Both automatic and human assessment found the AppTek translations to outperform competing submissions in terms of quality and the desired length, with performance matching "unconstrained" systems trained on significantly more data. An additional evaluation performed by the task organizers showed that creating synthetic speech from AppTek’s system output leads to automatically dubbed videos with a smooth speaking rate and of higher perceived quality than when using the competing systems.

"The superior performance of AppTek’s isometric speech translation system is another step towards delivering the next generation of speech-enabled technologies for the broadcast and media markets", said Kyle Maddock, AppTek’s SVP of Marketing. "We are committed to delivering the state-of-the-art for demanding markets such as media and entertainment, and isometric translation is a key component for more accurate automatic subtitling and dubbing workflows."

AppTek scientists Patrick Wilken and Evgeny Matusov will present the details of AppTek’s submission at this year’s IWSLT conference held in Dublin on May 26-27, 2022.

The full IWSLT 2022 results can be found here.

About AppTek
AppTek is a global leader in artificial intelligence (AI) and machine learning (ML) technologies for automatic speech recognition (ASR), neural machine translation (NMT), natural language processing/understanding (NLP/U) and text-to-speech (TTS) technologies.  The AppTek platform delivers industry-leading, real-time streaming and batch technology solutions in the cloud or on-premises for organizations across a breadth of global markets such as media and entertainment, call centers, government, enterprise business, and more. Built by scientists and research engineers who are recognized among the best in the world, AppTek’s multidimensional 4D for HLT (human language technology) solutions with slice and dice methodology covering hundreds of languages/dialects, domains, channels and demographics drive high impact results with speed and precision.  For more information, please visit

Media Contact:
Kyle Maddock