Application of Audio Fingerprinting Techniques for Real-Time Scalable Speech Retrieval and Speech Clusterization

TL;DR

This paper explores adapting audio fingerprinting methods for fast, scalable speech retrieval and clustering in telecommunications, bypassing speech-to-text conversion to enhance speed and efficiency.

Contribution

It introduces modifications to existing audio fingerprinting techniques tailored for speech, enabling batch processing and clustering without speech-to-text conversion.

Findings

Effective speech retrieval in noisy conditions

Clustering based on speech transcripts without transcription

No GPU required for real-time processing

Abstract

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.