音频指纹技术在实时可扩展语音检索与语音聚类中的应用
Application of Audio Fingerprinting Techniques for Real-Time Scalable Speech Retrieval and Speech Clusterization
AI总结：
本文针对电信与云平台中的语音检索与聚类，改进音频指纹技术，实现无需GPU的快速批处理，并支持基于转录的聚类。
摘要：
近年来，音频指纹技术取得了巨大进步，即使在查询音频样本严重退化或在嘈杂条件下录制的情况下，也能实现准确快速的音频检索。不出所料，现有工作大多围绕音乐展开，诸如苹果的Shazam或谷歌的Now Playing等流行音乐识别服务，专为移动设备上的单个音频识别而设计。然而，语音的频谱内容与音乐不同，因此需要对当前的音频指纹方法进行修改。本文为调整现有技术以应对电信和云通信平台中语音检索这一专门挑战提供了新的见解。重点在于实现批处理中的快速准确音频检索，而非促进单个请求，通常在集中式服务器上进行。此外，本文展示了如何利用这种方法，在不进行实际语音转文本转换的情况下，基于语音转录文本来支持音频聚类。这种优化使得处理速度显著加快，且无需GPU计算，而GPU计算通常是实时操作中先进语音转文本工具所需的条件。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
