{"id":15167,"date":"2024-02-28T15:33:55","date_gmt":"2024-02-28T20:33:55","guid":{"rendered":"https:\/\/www.3playmedia.com\/blog\/measuring-captioning-accuracy-why-wer-and-ner-analyses-differ\/"},"modified":"2025-08-11T16:12:52","modified_gmt":"2025-08-11T16:12:52","slug":"measuring-captioning-accuracy-why-wer-and-ner-analyses-differ","status":"publish","type":"post","link":"https:\/\/www.3playmedia.com\/blog\/measuring-captioning-accuracy-why-wer-and-ner-analyses-differ\/","title":{"rendered":"Measuring Captioning Accuracy: Why WER and NER Analyses Differ"},"content":{"rendered":"\n<div class=\"wp-block-group is-style-dark alignfull p-post-header has-global-padding is-content-justification-center is-layout-constrained wp-block-group-is-layout-constrained has-background is-style-dark--1\" style=\"padding-top:var(--wp--preset--spacing--50);padding-bottom:var(--wp--preset--spacing--60);background-image:url(&#039;\/wp-content\/themes\/core\/assets\/media\/backgrounds\/hero-wave-blog.svg&#039;);background-position:50% 50%;background-size:cover;\">\n\t<div class=\"wp-block-group aligngrid has-global-padding is-layout-constrained wp-block-group-is-layout-constrained\">\n\t\t<div class=\"wp-block-buttons aligngrid is-layout-flex wp-block-buttons-is-layout-flex\">\n\t\t\t<div class=\"wp-block-button is-style-ghost is-style-reversed is-style-ghost--2\"><a class=\"wp-block-button__link wp-element-button\" href=\"\/blog\">Blog<\/a><\/div>\n\t\t\t<\/div>\n\t\t\n\n\t\t\n\t\t<hr class=\"wp-block-separator has-alpha-channel-opacity aligngrid\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:0\"\/>\n\t\t\n\n\t\t\n\t\t<div class=\"wp-block-columns are-vertically-aligned-center aligngrid is-layout-flex wp-container-core-columns-is-layout-008400e6 wp-block-columns-is-layout-flex\" style=\"margin-top:var(--wp--preset--spacing--60);margin-bottom:0\">\n\t\t\t<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\"><div style=\"padding-bottom:0;padding-top:0;\" class=\"is-style-category wp-block-tribe-terms\"><ul class=\"wp-block-tribe-terms__list\"><li class=\"wp-block-tribe-terms__term\"><span class=\"wp-block-tribe-terms__link t-category\">Captioning<\/span><\/li><\/ul><\/div>\n\n\t\t\t\t<h1 style=\"margin-top:var(--wp--preset--spacing--30);margin-bottom:0;\" class=\"alignwide wp-block-post-title\">Measuring Captioning Accuracy: Why WER and NER Analyses Differ<\/h1>\n\n\t\t\t\t\n\t\t\t\t<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-9116416d wp-block-group-is-layout-flex\" style=\"margin-top:var(--wp--preset--spacing--30)\"><div class=\"has-text-align-left wp-block-post-author-name\"><a href=\"https:\/\/www.3playmedia.com\/blog\/author\/3playmedia\/\" target=\"_self\" class=\"wp-block-post-author-name__link\">3Play Media<\/a><\/div>\n\n\t\t\t\t\t\n\t\t\t\t\t<p>\u2022<\/p>\n\t\t\t\t\t\n\n\t\t\t\t\t<div class=\"has-text-align-left wp-block-post-date has-10-font-size\"><time datetime=\"2024-02-28T15:33:55+00:00\">February 28, 2024<\/time><\/div><\/div>\n\t\t\t\t\n\n\t\t\t\t<\/div>\n\t\t\t\n\n\t\t\t\n\t\t\t<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\"><figure class=\"alignfull alignfull wp-block-post-featured-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"500\" src=\"https:\/\/www.3playmedia.com\/wp-content\/uploads\/2024\/02\/accuracy-3.jpg\" class=\"attachment-post-thumbnail size-post-thumbnail wp-post-image\" alt=\"\" style=\"border-radius:1rem;object-fit:cover;\" srcset=\"https:\/\/www.3playmedia.com\/wp-content\/uploads\/2024\/02\/accuracy-3.jpg 1400w, https:\/\/www.3playmedia.com\/wp-content\/uploads\/2024\/02\/accuracy-3-300x107.jpg 300w, https:\/\/www.3playmedia.com\/wp-content\/uploads\/2024\/02\/accuracy-3-1024x366.jpg 1024w, https:\/\/www.3playmedia.com\/wp-content\/uploads\/2024\/02\/accuracy-3-768x274.jpg 768w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\" \/><\/figure><\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n                                                <hr \/>\n<p style=\"text-align: center\"><span style=\"text-decoration: underline\"><strong><span style=\"font-size: x-large\"><a href=\"https:\/\/go.3playmedia.com\/wp-best-practices\" target=\"_blank\" rel=\"noopener\">Captioning Best Practices for Media &amp; Entertainment [Free eBook]<\/a><\/span><\/strong><\/span><\/p>\n<hr \/>\n<p><span style=\"font-weight: 400\">When it comes to measuring captioning accuracy, there\u2019s no shortage of errors that need to be considered: punctuation, grammar, speaker identification, capitalization, and word errors, to name a few. But what does it mean when a captioning vendor says their captions are 99% accurate?\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">It turns out that \u201c99% accuracy\u201d can mean very different things depending on the model a vendor uses to measure said accuracy. Different vendors use different measurement models, which can contribute to confusion when percentages are marketed to describe the accuracy of closed captions. In this blog, we\u2019ll discuss the NER model and how it differs from two commonly used measurements, Word Error Rate (WER) and Formatted Error Rate (FER).<\/span><\/p>\n<h2><span style=\"font-weight: 400\">The NER Model<\/span><\/h2>\n<p><span style=\"font-weight: 400\">The NER model, which originated in Europe and <\/span><a href=\"https:\/\/www.nercanada.ca\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">is often used in Canada<\/span><\/a><span style=\"font-weight: 400\">, differs from the <\/span><a href=\"https:\/\/www.3playmedia.com\/blog\/captioning-accuracy-how-to-measure-error-rates\/\"><span style=\"font-weight: 400\">accuracy measurement rates commonly used in the United States<\/span><\/a><span style=\"font-weight: 400\">. In the U.S., all errors\u2014including spelling, punctuation, grammar, speaker identifications, word substitutions, omissions, and more\u2014are considered to obtain a percentage that measures the average accuracy of the closed captions on a piece of media.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">In contrast, NER scoring emphasizes meaning and how accurately ideas are captured in captions, making it an extremely subjective and legally risky measurement.<\/span> <span style=\"font-weight: 400\">For instance, <\/span><a href=\"https:\/\/www.3playmedia.com\/blog\/fccs-new-quality-standards-closed-captioning-video-programming\/\"><span style=\"font-weight: 400\">the FCC closed captioning guidelines state<\/span><\/a><span style=\"font-weight: 400\">, \u201cIn order to be accurate, captions must match the spoken words in the dialogue, in their original language (English or Spanish), to the fullest extent possible and include full lyrics when provided on the audio track.\u201d More specifically, the guidelines require captions to include all words spoken in the order spoken (i.e., no paraphrasing). <\/span><b>Considering the legal requirements of live and recorded captioning, the subjectivity of NER scoring makes it an inherently risky method.<\/b><\/p>\n<h3><b>How are NER Scores Calculated?<\/b><\/h3>\n<p><span style=\"font-weight: 400\">Vendors <\/span><a href=\"https:\/\/nercanada.ca\/ner-guidelines\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">grade each caption error<\/span><\/a><span style=\"font-weight: 400\"> based on its severity or resulting understandability when using the NER model. In many cases, vendors decide for themselves what constitutes a critical error. This subjectivity means that <\/span><b>a caption file could get different NER results depending on who scores the file\u2014contributing to significant liability for customers.<\/b><span style=\"font-weight: 400\">\u00a0<\/span><\/p>\n<div class=\"su-box su-box-style-default\" id=\"\" style=\"border-color:#b3003c;border-radius:5px\"><div class=\"su-box-title\" style=\"background-color:#E6006F;color:#FFFFFF;border-top-left-radius:3px;border-top-right-radius:3px\">The NER Calculation<\/div><div class=\"su-box-content su-u-clearfix su-u-trim\" style=\"border-bottom-left-radius:3px;border-bottom-right-radius:3px\">NER Score = (Words \u2013 NER Deductions) \/ Words * 100<\/div><\/div>\n<p><span style=\"font-weight: 400\">One of the reasons NER scores get inflated so quickly is that the denominator of the NER equation is all of the words written. However, the numerator, which is the number of correct words, also starts at the total count of words and is only deducted by fractions of certain words, even if a whole sentence is paraphrased or several words are wrong in sequence. In addition, the denominator is the total number of words captioned, not the total number of words that should have been captioned based on verbatim dialogue.<\/span><\/p>\n<hr \/>\n<p style=\"text-align: center\"><span class=\"su-highlight\" style=\"background:#DEEFF5;color:#000000\">&nbsp;<a href=\"\/\/go.3playmedia.com\/wp-best-practices&quot;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>Discover Captioning Best Practices for the Entertainment Industry<\/u><\/a> \u27a1\ufe0f&nbsp;<\/span>\n<hr \/>\n<h3><b>Types of NER Errors<img loading=\"lazy\" decoding=\"async\" class=\"size-thumbnail wp-image-33094 alignright\" src=\"https:\/\/www.3playmedia.com\/wp-content\/uploads\/2024\/02\/error-150x150.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/b><\/h3>\n<p><a href=\"https:\/\/speechtotextcaptioning.org\/resources\/Documents\/NER\/NER%20Live%20Captioning%20Page%201%20UPDATED.pdf\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">NER errors are categorized under two main types<\/span><\/a><span style=\"font-weight: 400\">, each with corresponding deduction values of either 0.0, 0.25, 0.5, or 1.0 (a full point deduction). In this way, the NER model functions more as a score than a percentage. Caption scoring begins at 100 and is graded according to the number of errors and their assigned score deductions.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Of note, the NER marking of \u201cCorrect Edition\u201d indicates that paraphrased captions capture the full meaning of the spoken content. <\/span><b>However, a Correct Edition marking might have a starkly decreased WER score with no deduction in the NER score. <\/b><span style=\"font-weight: 400\">At 3Play Media, we see many examples of this difference, which is consequential for accessibility and legal compliance with FCC standards and other legislation.<\/span><\/p>\n<div class=\"su-box su-box-style-default\" id=\"\" style=\"border-color:#b3003c;border-radius:5px\"><div class=\"su-box-title\" style=\"background-color:#E6006F;color:#FFFFFF;border-top-left-radius:3px;border-top-right-radius:3px\">NER vs. WER: Different Measurements Provide Different Results<\/div><div class=\"su-box-content su-u-clearfix su-u-trim\" style=\"border-bottom-left-radius:3px;border-bottom-right-radius:3px\">In conducting market research, we scored a Canadian government meeting transcription using the NER method and received a score of 99.00 (or \u201cvery good\u201d) because the captioner used a high degree of paraphrasing that was \u201cmostly successful.\u201d However, when we scored the same meeting using the WER method, we received an accuracy rating of 93.2%, which is not legally compliant under the FCC due to the number of captions that were paraphrased compared to the verbatim speech. We plan to conduct further research to analyze the measurement challenges of NER vs. WER.<\/div><\/div>\n<p><b>Edition errors<\/b><span style=\"font-weight: 400\"> represent the loss of an idea unit or piece of information. They include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Critical Error (False Information): An editing or paraphrasing error provides false but plausible information (-1.0)<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Major Error (Loss of Main Point): Inaccurate captions lose the main point of an idea (-0.5)<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Minor Error (Loss of Detail): Inaccurate captions keep the main point but lose a detail (-0.25)<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Correct Edition: Paraphrase captures the full meaning (0.0)<\/span><\/li>\n<\/ul>\n<p><b>Recognition errors<\/b><span style=\"font-weight: 400\"> represent misrecognition of the spoken content. They include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Critical Error (False Information): A wrong word, phrase, or punctuation error provides false but plausible information (-1.0)<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Major Error (Nonsense Error): A wrong word, phrase, or punctuation affects comprehension of an idea. (-0.5)<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Minor Error (Benign Error): A wrong word, phrase, or punctuation affects readability but not comprehension. (-0.25)<\/span><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400\">Word Error Rate (WER) and Formatted Error Rate (FER)<\/span><\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-thumbnail wp-image-33096 alignright\" src=\"https:\/\/www.3playmedia.com\/wp-content\/uploads\/2024\/02\/accuracy-1-150x150.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<p><span style=\"font-weight: 400\">More commonly, captioning accuracy for recorded content is often made up of two pieces: Word Error Rate (WER) and Formatted Error Rate (FER). WER is the standard measure of transcription accuracy and considers the number of inaccurate words versus the total number of words. In contrast, FER is the percentage of word errors when formatting elements such as punctuation, grammar, speaker identification, non-speech elements, capitalization, and other notations are taken into account.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">For closed captioning, <\/span><a href=\"https:\/\/www.3playmedia.com\/blog\/fccs-new-quality-standards-closed-captioning-video-programming\/\"><span style=\"font-weight: 400\">the FCC<\/span><\/a><span style=\"font-weight: 400\"> mandates all of these formatting requirements to achieve at least 99% accuracy for recorded content. For recorded and live content, the FCC quality standards do not permit the same amount of flexibility that the NER model allows. While live captioning does not have firm accuracy standards and instead relies on best practices,<\/span> <a href=\"https:\/\/www.law.cornell.edu\/cfr\/text\/47\/79.1\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">the FCC still focuses<\/span><\/a><span style=\"font-weight: 400\"> on accuracy, synchronicity, completeness, and placement\u2014which are more aligned with WER and FER than NER.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400\">WER and FER vs. NER: Unequal Measures of Accuracy and Quality<\/span><\/h2>\n<p><span style=\"font-weight: 400\">Compared to WER and FER, NER is not an equivalent measure of accuracy or quality. While captioning with a high NER score may be useful for <\/span><a href=\"https:\/\/www.3playmedia.com\/blog\/summarized-transcription-vs-real-time-captioning\/\"><span style=\"font-weight: 400\">viewers who value overall meaning instead of absolute accuracy<\/span><\/a><span style=\"font-weight: 400\">, higher WER and FER measurements are essential for d\/Deaf and hard-of-hearing viewers and legal compliance.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Some vendors do not state which model they\u2019re using in determining accuracy for recorded captions, which is a misleading practice and can put your content at risk for litigation. <\/span><b>When evaluating a potential vendor, you should always inquire about the models they use to determine the accuracy of their captions.<\/b><\/p>\n<p><span style=\"font-weight: 400\">Additionally, NER scoring is more beneficial for live content and less applicable to recorded content, so be wary when a vendor uses NER to describe accuracy for recorded captioning. There are inherent <\/span><a href=\"https:\/\/www.3playmedia.com\/blog\/how-to-handle-live-closed-captioning-and-the-challenges\/\"><span style=\"font-weight: 400\">challenges in captioning live content<\/span><\/a><span style=\"font-weight: 400\">, and recorded captions should be measured differently because the captioner has more time and can perfect the verbatim transcription. NER scoring, if used, should always be near perfect for recorded content because a recorded captioner should never need to summarize the spoken content\u2014captions should achieve verbatim accuracy and, by doing so, retain meaning.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Ultimately, when offering closed captioning as an accommodation, the best practice is often to provide an equitable experience by presenting content as spoken, which necessitates using WER and FER to measure accuracy.<\/span><\/p>\n<hr \/>\n<p><a href=\"https:\/\/go.3playmedia.com\/wp-best-practices\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-28766\" src=\"https:\/\/www.3playmedia.com\/wp-content\/uploads\/2019\/02\/CC-best-practices-CTA-white.jpg\" alt=\"Closed Captioning Best Practices for Media and Entertainment: Read the eBook\" width=\"1000\" height=\"286\" \/><\/a><\/p>\n\n\n                                                \n                                                <hr class=\"wp-block-separator has-alpha-channel-opacity\" style=\"margin-bottom:0\" \/>\n                                                \n\n                                                \n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-6c28011a wp-block-group-is-layout-constrained\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:0;padding-right:0;padding-bottom:0;\">\n\t<p style=\"margin-top:0\">Filed under<\/p>\n\t\n\n<div style=\"margin-top:var(--wp--preset--spacing--20);\" class=\"is-style-pills wp-block-tribe-terms\"><ul class=\"wp-block-tribe-terms__list\"><li class=\"wp-block-tribe-terms__term\"><a href=\"https:\/\/www.3playmedia.com\/blog\/tag\/accuracy\/\" class=\"wp-block-tribe-terms__link t-category\">Accuracy<\/a><\/li><li class=\"wp-block-tribe-terms__term\"><a href=\"https:\/\/www.3playmedia.com\/blog\/tag\/blog\/\" class=\"wp-block-tribe-terms__link t-category\">Blog<\/a><\/li><li class=\"wp-block-tribe-terms__term\"><a href=\"https:\/\/www.3playmedia.com\/blog\/tag\/captions\/\" class=\"wp-block-tribe-terms__link t-category\">captions<\/a><\/li><li class=\"wp-block-tribe-terms__term\"><a href=\"https:\/\/www.3playmedia.com\/blog\/tag\/live-captions\/\" class=\"wp-block-tribe-terms__link t-category\">live captions<\/a><\/li><\/ul><\/div><\/div>\n\n\n\n                                                \n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-dda23514 wp-block-group-is-layout-constrained\" style=\"margin-top:var(--wp--preset--spacing--40);padding-top:var(--wp--preset--spacing--40);padding-right:0;padding-bottom:var(--wp--preset--spacing--40);\">\n<p style=\"margin-top:0\">About the author<\/p>\n\n\n<div style=\"margin-top:var(--wp--preset--spacing--20);\" class=\"wp-block-post-author\"><div class=\"wp-block-post-author__avatar\"><img alt='' src='https:\/\/secure.gravatar.com\/avatar\/077af0eed48d15985c692daeff79eaff223726049f53bd6ef9a51ffb76629ed1?s=96&#038;d=mm&#038;r=g' srcset='https:\/\/secure.gravatar.com\/avatar\/077af0eed48d15985c692daeff79eaff223726049f53bd6ef9a51ffb76629ed1?s=192&#038;d=mm&#038;r=g 2x' class='avatar avatar-96 photo' height='96' width='96' \/><\/div><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\"><a href=\"https:\/\/www.3playmedia.com\/blog\/author\/3playmedia\/\" target=\"_self\">3Play Media<\/a><\/p><p class=\"wp-block-post-author__bio\"><\/p><\/div><\/div><\/div>\n\n\n\n                                                \n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-fe217b39 wp-block-group-is-layout-constrained\">\n<p style=\"margin-bottom:0\">Share this page<\/p>\n\n\n\n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-fe217b39 wp-block-group-is-layout-constrained\">\n<p><\/p>\n\n\n\n<ul class=\"wp-block-outermost-social-sharing has-normal-icon-size has-icon-color is-style-logos-only is-content-justification-left is-layout-flex wp-container-outermost-social-sharing-is-layout-95ca00fe wp-block-social-sharing-is-layout-flex\" style=\"margin-top:var(--wp--preset--spacing--10)\"><li style=\"color: #767676; 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Entertainment [Free eBook] When it comes to measuring captioning accuracy, there\u2019s no shortage of errors that need to be considered: punctuation, grammar, speaker identification, capitalization, and word errors, to name a few. But what does it mean when a captioning vendor says their captions are 99% accurate?\u00a0 It turns&#8230;<\/p>\n","protected":false},"author":63,"featured_media":2430,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[133],"tags":[82,8,81,74],"industry":[],"resource_type":[92],"class_list":["post-15167","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-captioning","tag-accuracy","tag-blog","tag-captions","tag-live-captions"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.0 (Yoast SEO v26.0) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Measuring Captioning Accuracy: Why WER and NER Analyses Differ<\/title>\n<meta name=\"description\" content=\"In this blog, we dive into the differences between NER vs. WER, two different models used to measure the quality of closed captions.\" 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