文本无关说话人识别中句级特征提取方法研究综述EI北大核心CSCD |
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引用本文: | 陈晨,韩纪庆,陈德运,何勇军.文本无关说话人识别中句级特征提取方法研究综述EI北大核心CSCD[J].自动化学报,2022,48(3):664-688. |
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作者姓名: | 陈晨 韩纪庆 陈德运 何勇军 |
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作者单位: | 1.哈尔滨理工大学计算机科学与技术博士后流动站 哈尔滨 150080 |
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基金项目: | 国家自然科学基金(62101163);;黑龙江省自然科学基金(LH2021F029);;中国博士后科学基金(2021M701020);;黑龙江省博士后专项经费(LBH-Z20020); |
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摘 要: | 句级(Utterance-level)特征提取是文本无关说话人识别领域中的重要研究方向之一.与只能刻画短时语音特性的帧级(Frame-level)特征相比,句级特征中包含了更丰富的说话人个性信息;且不同时长语音的句级特征均具有固定维度,更便于与大多数常用的模式识别方法相结合.近年来,句级特征提取的研究取得了很大的进展,鉴于其在说话人识别中的重要地位,本文对近期具有代表性的句级特征提取方法与技术进行整理与综述,并分别从前端处理、基于任务分段式与驱动式策略的特征提取方法,以及后端处理等方面进行论述,最后对未来的研究趋势展开探讨与分析.
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关 键 词: | 说话人识别 句级特征提取 任务分段式策略 任务驱动式策略 联合学习 |
收稿时间: | 2020-07-09 |
Utterance-level Feature Extraction in Text-independent Speaker Recognition: A Review |
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Affiliation: | 1.Postdoctoral Research Station of Computer Science and Technology, Harbin University of Science and Technology, Harbin 1500802.School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001 |
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Abstract: | Utterance-level feature extraction is one of the most important researches in text-independent speaker recognition. Compared with the frame-level features which only contain the short-term speech characteristics, the utterance-level features can effectively capture more speaker discriminative information. Meanwhile, it also has another advantage that any utterance with a variable duration can be represented as a fixed-dimension feature. Thus, the utterance-level features are easy to integrate with most commonly-used pattern recognition methods. In recent years, the researches on utterance-level feature extraction have made great progress. Considering the importance of utterance-level feature extraction in speaker recognition, this paper will organize and summarize the typical methods. Specifically, the front-end processing, the feature extraction based on the task-segmented strategy and task-driven strategy, and the back-end processing are introduced respectively. Finally, the future trends in speaker recognition are discussed and analyzed. |
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