{"id":40269,"date":"2026-04-08T22:09:23","date_gmt":"2026-04-08T20:09:23","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/speech-ai-works-in-demos-so-why-does-it-break-in-real-life\/"},"modified":"2026-04-08T22:09:23","modified_gmt":"2026-04-08T20:09:23","slug":"speech-ai-works-in-demos-so-why-does-it-break-in-real-life","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/speech-ai-works-in-demos-so-why-does-it-break-in-real-life\/","title":{"rendered":"Speech AI Works In Demos\u2026 So Why Does It Break In Real Life?"},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>Been looking closely at speech datasets lately, and something feels off.<\/p>\n<p>Most of what\u2019s used to train models is way too clean.<\/p>\n<p>No interruptions.<br \/> No overlap.<br \/> Hardly any code-switching.<\/p>\n<p>But that\u2019s not how people actually speak, especially in India.<\/p>\n<p>Real conversations are messy. People switch between Hindi and English mid-sentence, talk over each other, drop context, pick it back up.<\/p>\n<p>Feels like models aren\u2019t failing because of architecture, but because the data doesn\u2019t reflect reality.<\/p>\n<p>Curious how others here are dealing with this.<br \/> Are you seeing the same gap in real-world performance?<\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/Cautious-Today1710\"> \/u\/Cautious-Today1710 <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1sg1sqi\/speech_ai_works_in_demos_so_why_does_it_break_in\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1sg1sqi\/speech_ai_works_in_demos_so_why_does_it_break_in\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-40269 jlk' href='javascript:void(0)' data-task='like' data-post_id='40269' data-nonce='65e0e39b87' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-40269 lc'>0<\/span><\/a><\/div><\/div> <div class='status-40269 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>Been looking closely at speech datasets lately, and something feels off. Most of what\u2019s used to train&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-40269","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/40269","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=40269"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/40269\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=40269"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=40269"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=40269"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}