多通道语音增强用于自动语音识别:文献综述
Zubair Zaland1, Mumtaz Begum Mustafa1, Miss Laiha Mat Kiah2
1Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
PeerJ. Computer science
|June 26, 2025
概括
本综述系统地分析了用于自动语音识别 (ASR) 的多通道语音增强 (MCSE). 它确定了有效的MCSE方法,并强调了噪音概括的挑战,以提高ASR性能.
科学领域:
- 信号处理 信号处理
- 人工智能的人工智能
- 声学 声学 在声学方面
背景情况:
- 多通道语音增强 (MCSE) 对于强大的自动语音识别 (ASR) 是至关重要的.
- 现有的审查缺乏专门针对ASR应用的MCSE的系统分析.
- 在MCSE方法,模型和数据集的快速进步需要一个全面的概述.
研究的目的:
- 对ASR系统的MCSE方法进行系统审查.
- 分析MCSE和ASR在各种技术,模型和噪声条件中的表现.
- 讨论当前的挑战,局限性和未来的研究方向在MCSE的ASR.
主要方法:
- 在主要电子数据库 (谷歌学者,IEEE Xplore等) 中使用关键词搜索进行系统的文献审查. ) 的情况.
- 包含和排除标准适用于240篇初始文章,最终产生40篇实验性文章 (23期刊,17次会议).
- 落后的雪球和质量评估被用来完成文章的选择.
主要成果:
- 在ASR研究的MCSE中观察到越来越多的趋势,文字错误率 (WER),语音质量的感知评估 (PESQ) 和短期目标智能 (STOI) 是常见的绩效指标.
- 发现的一个重大挑战是MCSE研究缺乏普遍性和可比性,这阻碍了针对语音识别噪音的统一解决方案.
- 确定了在各种模型,技术,噪音类型和环境中提高ASR性能的关键MCSE方法.
结论:
- 本综述提供了对MCSE和ASR技术的全面审查.
- 识别的MCSE方法在各种条件下提供了改善ASR性能的潜力.
- 未来的研究应该解决一般性和可比性问题,以开发更强大和统一的MCSE解决方案.
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