@inproceedings{c1f39fd422e349848cdfb2a8968d9e5c,
title = "A thousand words are worth more than one recording: Word-embedding based speaker change detection",
abstract = "Speaker Change Detection (SCD) is the task of segmenting an input audio-recording according to speaker interchanges. This task is essential for many applications, such as automatic voice transcription or Speaker Diarization (SD). This paper focuses on the essential task of audio segmentation and suggests a word-embedding-based solution for the SCD problem. Moreover, we show how to use our approach in order to outperform voice-based solutions for the SD problem. We empirically show that our method can accurately identify the speaker-turns in an audio-recording with 82.12\% and 89.02\% success in the Recall and F1-score measures.",
keywords = "Clustering, Speaker change detection, Speaker diarization, Speech recognition, Word embedding",
author = "Anidjar, \{Or Haim\} and Itshak Lapidot and Chen Hajaj and Amit Dvir",
note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 ; Conference date: 30-08-2021 Through 03-09-2021",
year = "2021",
doi = "10.21437/Interspeech.2021-87",
language = "אנגלית",
series = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
pages = "3121--3125",
booktitle = "22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021",
}