用音频指纹技术实现云端语音快速检索与聚类
Application of Audio Fingerprinting Techniques for Real-Time Scalable Speech Retrieval and Speech Clusterization
- 基于音频指纹改进语音检索方法，适应语音频谱特性
- 支持批量处理，无需转写即可完成语音聚类
- 无需GPU加速，适合实时语音系统部署
近年来，音频指纹技术取得了显著进展，能够在音频严重劣化或噪声环境下仍实现准确快速的音频检索。现有工作大多聚焦于音乐识别，如Apple的Shazam或Google的Now Playing，主要面向移动端单次音频识别。然而，语音的频谱特征与音乐不同，需对现有方法进行调整。本文提出将音频指纹技术应用于电信及云通信平台中的语音检索任务，重点实现批量处理下的快速精准检索，而非单次请求。此外，论文展示如何利用该方法在不进行实际语音转文字的前提下，基于语音转录内容实现音频聚类。该优化使处理速度显著提升，且无需依赖GPU计算，满足实时运行需求，突破了当前先进语音转写工具对高性能硬件的依赖。
原文摘要 · Abstract (English)
Audio fingerprinting techniques have seen great advances in recent years, enabling accurate and fast audio retrieval even in conditions when the queried audio sample has been highly deteriorated or recorded in noisy conditions. Expectedly, most of the existing work is centered around music, with popular music identification services such as Apple's Shazam or Google's Now Playing designed for individual audio recognition on mobile devices. However, the spectral content of speech differs from that of music, necessitating modifications to current audio fingerprinting approaches. This paper offers fresh insights into adapting existing techniques to address the specialized challenge of speech retrieval in telecommunications and cloud communications platforms. The focus is on achieving rapid and accurate audio retrieval in batch processing instead of facilitating single requests, typically on a centralized server. Moreover, the paper demonstrates how this approach can be utilized to support audio clustering based on speech transcripts without undergoing actual speech-to-text conversion. This optimization enables significantly faster processing without the need for GPU computing, a requirement for real-time operation that is typically associated with state-of-the-art speech-to-text tools.
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