{"id":932,"date":"2026-05-22T13:13:21","date_gmt":"2026-05-22T13:13:21","guid":{"rendered":"https:\/\/kb.dictalogic.com\/?p=932"},"modified":"2026-05-22T13:13:21","modified_gmt":"2026-05-22T13:13:21","slug":"how-to-understand-speaker-diarization-and-identification-in-conversation-to-text","status":"publish","type":"post","link":"https:\/\/kb.dictalogic.com\/index.php\/2026\/05\/22\/how-to-understand-speaker-diarization-and-identification-in-conversation-to-text\/","title":{"rendered":"How to understand speaker diarization and identification in Conversation to Text"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\"><strong>Overview<\/strong><\/h2>\n\n\n\n<p>This article explains what speaker diarization and speaker identification are, how they work in Conversation to Text, and what to expect from each feature.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Applies to<\/strong><\/h2>\n\n\n\n<p>All Users<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What is speaker diarization?<\/strong><\/h2>\n\n\n\n<p>Speaker diarization is the process by which the AI engine automatically detects that different people are speaking within a recording and separates their contributions in the transcript. It answers the question: &#8220;Who spoke when?&#8221;<\/p>\n\n\n\n<p>When diarization is applied, the transcript is formatted so that each segment of speech is attributed to a distinct speaker label typically shown as &#8220;Speaker 1&#8221;, &#8220;Speaker 2&#8221;, &#8220;Speaker 3&#8221;, and so on. The system identifies speaker boundaries by analysing the acoustic characteristics of each voice&nbsp; factors such as pitch, tone, and rhythm&nbsp; and groups segments spoken by the same person together under the same label.<\/p>\n\n\n\n<p>Diarization does not require any prior information about the speakers. It works entirely from the audio content of the recording.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What is speaker identification?<\/strong><\/h2>\n\n\n\n<p>Speaker identification goes one step further than diarization. Where speaker profiles&nbsp; essentially voice samples or voice prints have been set up for known individuals within the Dictalogic system, the engine can attempt to match the detected voices in a recording to those named profiles. Rather than labelling speakers as &#8220;Speaker 1&#8221; and &#8220;Speaker 2&#8221;, the system will label them with the corresponding individuals&#8217; names.<\/p>\n\n\n\n<p>Speaker identification requires prior enrolment of voice profiles for the individuals concerned. It is an optional enhancement on top of diarization.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How they work together<\/strong><\/h2>\n\n\n\n<p>In a typical Conversation to Text workflow: diarization runs automatically during transcription and separates the conversation into speaker attributed segments. If speaker profiles are available and identification is enabled, the system attempts to match each speaker segment to a named profile. Where a match cannot be made confidently, the speaker will remain labelled generically. After transcription, the user can manually review and correct speaker labels within the transcript editor.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Limitations<\/strong><\/h2>\n\n\n\n<p>Diarization accuracy can be affected by poor audio quality, overlapping speech, speakers with very similar voices, background noise, and very short speaker turns. It is always recommended to review speaker labels after transcription.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Overview This article explains what speaker diarization and speaker identification are, how they work in Conversation to Text, and what to expect from each feature. Applies to All Users What is speaker diarization? Speaker diarization is the process by which the AI engine automatically detects that different people are speaking within a recording and separates [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[30],"tags":[],"_links":{"self":[{"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/posts\/932"}],"collection":[{"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/comments?post=932"}],"version-history":[{"count":2,"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/posts\/932\/revisions"}],"predecessor-version":[{"id":1619,"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/posts\/932\/revisions\/1619"}],"wp:attachment":[{"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/media?parent=932"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/categories?post=932"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kb.dictalogic.com\/index.php\/wp-json\/wp\/v2\/tags?post=932"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}