EchoNet++:一个多语言的足球比赛音频评论数据集.
Fahad Majeed1, Maria Nazir2, Marco Agus3
1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar. fama44316@hbku.edu.qa.
Scientific reports
|February 17, 2026
概括
这项研究引入了足球广播的音频分析管道,改进了语音识别和翻译. 该系统为专业体育媒体提供可扩展的,语言无关的音频理解.
科学领域:
- * 多模式信号处理
- * 计算语言学计算语言学
- * 运动分析.
背景情况:
- * 专业足球广播呈现复杂的音频环境,多个重叠的语音和非语音信号.
- * 现有的音频分析方法经常与多语言内容和各种声学条件作斗争.
- *自动语音识别 (ASR) 和翻译对于内容分析至关重要,但需要强大的管道.
研究的目的:
- * 开发和评估专业足球比赛广播的全面,可重复的音频分析管道.
- *系统地分析单个管道组件 (预处理,细分,转录,翻译) 对整体性能的影响.
- *为广播内容提供可扩展的,不依赖语言的音频理解.
主要方法:
- * 一个统一的管道用于音频提取,无声化 (Demucs),语音细分 (Silero VAD) 和评论员/观众流分类.
- *频域转换 (FFT) 和带宽过 (300 Hz-7 kHz) 用于语音信号的隔离.
- *使用各种自动语音识别 (ASR) 模型 (低声变体,疯狂快速低声) 和英语翻译的多语言转录.
主要成果:
- * 在完全匹配的视频中,在各种声条件下实现了较低的单词错误率和准确的语言检测.
- * 演示了一致的语音细分和评论员/观众音频流的可靠分类.
- *成功处理多语言音频,生成结构化的JSON输出,包含转录,翻译和元数据.
结论:
- * 开发的管道为体育广播中的自动音频处理提供了强大的框架.
- *系统组件分析为优化ASR和翻译性能在现实场景中提供了见解.
- *对数据集和管道的公开发布将促进可重复的研究和下游应用,如总结和分析.
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