A pp T ek’s Automatic Speech Translation: Generating Accurate and Well-Readable Subtitles (2025)

Abstract: We describe AppTek’s submission to the subtitling track of the IWSLT 2025 evaluation. We enhance our cascaded speech translation approach by adapting the ASR and the MT models on in-domain data. All components, including intermediate steps such as subtitle source language template creation and line segmentation, are optimized to ensure that the resulting target language subtitles respect the subtitling constraints not only on the number of characters per line and the number of lines in each subtitle block, but also with respect to the desired reading speed. AppTek’s machine translation with length control plays the key role in this process, effectively condensing subtitles to these constraints. Our experiments show that this condensation results in high-quality translations that convey the most important information, as measured by metrics such as BLEU or BLEURT, as well as the primary metric subtitle edit rate (SubER).

## introduction subtitle translation is a complex task that includes much more than recognizing what was uttered in an audio/video recording and translating it into the target language. accurate timing of the subtitles, segmentation into syntactically and/or semantically coherent units, and comfortable reading speed are important aspects affecting the viewing experience, in addition to mere translation quality @xcite . in this paper, we describe how apptek approaches the task with our in-house automatic speech recognition (asr) and neural machine translation (nmt) systems, which we couple with our intelligent subtitle line segmentation algorithm @xcite . in addition to algorithmic and modeling improvements, our unconstrained submissions benefit from in-domain data that was either available to us or was automatically extracted from public (parallel) data. the paper is structured as follows. in the following section, we describe the three main components of our subtitle translation approach: speech recognition in section 2.1, machine translation in 2.2 and subtitle segmentation in 2.3. section 3 gives details of our domain adaptation strategy for adapting to the entertainment and financial news domains which includes the usage of domain tags and fine-tuning on in-domain data. the effectiveness of domain adaptation is supported with experimental results at the end of the section. next, we focus on the newest enhancements of our system with regard to subtitling constraints -the restrictions on the maximum number of characters per line (cpl), lines per subtitle block (lpb), and the desired maximum reading speed measured in characters per second (cps). section 4 explains in detail our approach of space-constrained mt, which includes elaborate mt length control combined with targeted re-translation and line segmentation optimizations. the trade-off between translation quality and compliance with the constraints such as the reading speed is explained at the end of the section, with experimental results showing how apptek's nmt produces condensed translations that fulfill subtitling constraints just like subtitles from a professional subtitle translator, yet without a significant drop of the core mt quality. finally, we summarize our findings in section 5. ## subtitle translation we follow a cascaded speech translation approachfirst, the speech signal is automatically transcribed into an english subtitle file, which is then automatically translated into the target language. additional challenges related to subtitles are handled between these two components and after one or more translations of an utterance are obtained. ## domain adaptation in the following we describe how we adapt our system to the specific domains of the subtitling tasks of this year. ## subtitle condensation subtitles do not only need to provide accurate translations, they also need to follow readability constraints to not hinder an immersive viewing experience. in last year's edition of the subtitling track, only one out of eight submitted systems for the english-to-german task achieved a compliance with the desired maximum reading speed of 21 characters per second (cps) for more than 80% of the subtitles. we therefore focus on the reading speed constraint this year while at the same time aim to keep translation quality at a high level. ## conclusions this paper presented apptek's submission for the unconstrained subtitling track of the iwslt 2025. our focus this year was to a) boost translation quality by domain-and style-specific adaptation, and b) deliver readable subtitles that adhere to given space and reading-speed constraints. our key findings are as follows: together, these methods result a substantial boost in suber -the track's primary evaluation metric.