Closed captions have evolved from manual stenography to automated ASR systems over the past 50 years. While ASR accuracy has improved, challenges persist with formatting, space limitations, and verbatim transcription that includes unnecessary speech elements. AppTek's solution uses neural sequence-to-sequence translation to condense text by removing low-information content and controlling output length based on reading speed requirements. Trained on professional and synthetic data, this system improves comprehension while maintaining audio synchronization, offering a faster alternative to LLM-based approaches for both live and pre-recorded captioning.
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AppTek's Neural Machine Translation (NMT) system was enhanced to better handle specialized content by incorporating three key features during the training phase: glossary integration for consistent terminology translation, markup awareness for proper formatting tag placement, and placeholder handling to maintain code-related variables.
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The SubER (Subtitle Edit Rate) metric is a new standard designed to evaluate the quality of automatically generated subtitles by considering both textual accuracy and timing, addressing unique challenges in subtitle creation. It calculates the edits needed—such as substitutions, deletions, and shifts—to align machine-generated subtitles with a professional reference, offering a more holistic approach than traditional metrics like WER or BLEU. With broad applications in benchmarking and quality assurance, SubER has the potential to become an industry standard for subtitle evaluation, improving the viewing experience by ensuring subtitles are accurate and well-timed.
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Large Language Models (LLMs) is the latest milestone in Natural Language Processing (NLP) and the topic that has captivated the headlines and the attention of the scientific community the world over since the launch of ChatGPT approximately a year and a half ago. Despite the yet unresolved issue of hallucinations which has slowed down the faster and wider adoption of LLMs across all areas of our digital life, the latter have demonstrated impressive capabilities in a wide array of scenarios and tasks, including translation, making our imaginations run wild about the future of multilingual communication.
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We are pleased to announce our new patent(No: US 12,073,177 B2) dated August 27, 2024, for a method and apparatus for improved automatic subtitle segmentation using an artificial neural model.
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AppTek.ai 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), large language models (LLMs) and text-to-speech (TTS) technologies. The AppTek platform delivers industry-leading solutions 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 solutions cover a wide array of languages/ dialects, channels, domains and demographics.
