{"published":"2025-12-16 12:00:00","author_name":"revcomm-tech","version":"1.0","categories":["English","\u751f\u6210AI","Research","\u97f3\u58f0\u89e3\u6790"],"blog_title":"RevComm Tech Blog","provider_url":"https://hatena.blog","author_url":"https://blog.hatena.ne.jp/revcomm-tech/","blog_url":"https://tech.revcomm.co.jp/","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/r/revcomm-tech/20251212/20251212163933.png","url":"https://tech.revcomm.co.jp/speaker-diarization-with-language-model","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Ftech.revcomm.co.jp%2Fspeaker-diarization-with-language-model\" title=\"Speaker Diarization with Language Model - RevComm Tech Blog\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","description":"Background Speaker diarization has become increasingly valuable in applications designed for high-noise environments, often tailored for complex audio settings, emphasizing robust audio processing capabilities. Initially, the development of these systems centered on audio data alone. However, there \u2026","title":"Speaker Diarization with Language Model","width":"100%","height":"190","provider_name":"Hatena Blog","type":"rich"}