通过自调整语音分割和嵌入式提取实现对话短语说话者日记化

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-09-03 DOI:10.1109/LSP.2024.3453772
Haitian Lu;Gaofeng Cheng;Yonghong Yan
{"title":"通过自调整语音分割和嵌入式提取实现对话短语说话者日记化","authors":"Haitian Lu;Gaofeng Cheng;Yonghong Yan","doi":"10.1109/LSP.2024.3453772","DOIUrl":null,"url":null,"abstract":"Conversational short-phrase speaker diarization focuses on diarizing the phrases that are short in duration. Nonetheless, conventional speaker diarization systems fail to give enough importance to conversational short phrases. This letter proposed a novel speaker diarization system to address this issue. Firstly, we employ an RNN-T model for joint speech recognition and speaker change detection. The speech recognition results can be utilized directly in downstream tasks while the speaker change points serve as guidance for the following steps. Secondly, we introduce self-adjusting speech segmentation, which dynamically adjusts segment lengths based on the temporal distribution of speaker change points. Thirdly, we introduce self-adjusting embedding extraction, which employs speaker encoders trained under different speech duration conditions by projecting them to the same embedding space. Our method achieves a major reduction of Diarization Error Rate (DER) and Conversational Diarization Error Rate (CDER) on the MagicData-RAMC and Mixer 6 datasets.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Conversational Short-Phrase Speaker Diarization via Self-Adjusting Speech Segmentation and Embedding Extraction\",\"authors\":\"Haitian Lu;Gaofeng Cheng;Yonghong Yan\",\"doi\":\"10.1109/LSP.2024.3453772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Conversational short-phrase speaker diarization focuses on diarizing the phrases that are short in duration. Nonetheless, conventional speaker diarization systems fail to give enough importance to conversational short phrases. This letter proposed a novel speaker diarization system to address this issue. Firstly, we employ an RNN-T model for joint speech recognition and speaker change detection. The speech recognition results can be utilized directly in downstream tasks while the speaker change points serve as guidance for the following steps. Secondly, we introduce self-adjusting speech segmentation, which dynamically adjusts segment lengths based on the temporal distribution of speaker change points. Thirdly, we introduce self-adjusting embedding extraction, which employs speaker encoders trained under different speech duration conditions by projecting them to the same embedding space. Our method achieves a major reduction of Diarization Error Rate (DER) and Conversational Diarization Error Rate (CDER) on the MagicData-RAMC and Mixer 6 datasets.\",\"PeriodicalId\":13154,\"journal\":{\"name\":\"IEEE Signal Processing Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Signal Processing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10663942/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10663942/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

会话短语说话者日记法侧重于记录持续时间较短的短语。然而,传统的说话者日记系统未能对会话短语给予足够的重视。针对这一问题,本文提出了一种新型的说话者日记系统。首先,我们采用 RNN-T 模型进行联合语音识别和说话人变化检测。语音识别结果可直接用于下游任务,而说话人变化点则可为后续步骤提供指导。其次,我们引入了自调整语音分割技术,根据说话人变化点的时间分布动态调整分割长度。第三,我们引入了自调整嵌入提取,通过将在不同语音时长条件下训练的扬声器编码器投射到相同的嵌入空间,从而使用扬声器编码器。在 MagicData-RAMC 和 Mixer 6 数据集上,我们的方法大大降低了 Diarization Error Rate (DER) 和 Conversational Diarization Error Rate (CDER)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Conversational Short-Phrase Speaker Diarization via Self-Adjusting Speech Segmentation and Embedding Extraction
Conversational short-phrase speaker diarization focuses on diarizing the phrases that are short in duration. Nonetheless, conventional speaker diarization systems fail to give enough importance to conversational short phrases. This letter proposed a novel speaker diarization system to address this issue. Firstly, we employ an RNN-T model for joint speech recognition and speaker change detection. The speech recognition results can be utilized directly in downstream tasks while the speaker change points serve as guidance for the following steps. Secondly, we introduce self-adjusting speech segmentation, which dynamically adjusts segment lengths based on the temporal distribution of speaker change points. Thirdly, we introduce self-adjusting embedding extraction, which employs speaker encoders trained under different speech duration conditions by projecting them to the same embedding space. Our method achieves a major reduction of Diarization Error Rate (DER) and Conversational Diarization Error Rate (CDER) on the MagicData-RAMC and Mixer 6 datasets.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
期刊最新文献
KFA: Keyword Feature Augmentation for Open Set Keyword Spotting RFI-Aware and Low-Cost Maximum Likelihood Imaging for High-Sensitivity Radio Telescopes Audio Mamba: Bidirectional State Space Model for Audio Representation Learning System-Informed Neural Network for Frequency Detection Order Estimation of Linear-Phase FIR Filters for DAC Equalization in Multiple Nyquist Bands
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1