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.

翻译：近年来，音频指纹技术取得了重大进展，即使在查询音频样本严重劣化或在嘈杂环境下录制的情况下，也能实现准确快速的音频检索。可以预见的是，现有工作大多围绕音乐展开，例如Apple的Shazam或Google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，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。重点是在批处理中实现快速准确的音频检索，而非促进单个请求的处理，这通常是在集中式服务器上进行的。此外，本文展示了如何利用这种方法来支持基于语音转录本的音频聚类，而无需进行实际的语音到文本转换。这种优化显著加快了处理速度，且无需GPU计算，而GPU计算通常是实现实时操作的最先进语音到文本工具所必需的。