Abstract: Audio fingerprinting methods identify audio based on its content, even when the content has been possibly corrupted. Despite their applicability and usage in the speech domain, research and advancements in audio fingerprinting traditionally focus on music and are almost exclusively evaluated on music. We provide insights into speech-oriented audio fingerprinting by benchmarking the best performing audio fingerprinting models on speech data while taking into consideration the effects of noise, reverberation, temporal changes, pitch modifications, and lossy compression. Our findings indicate that audio fingerprinting methods based on self-supervised learning outperform classical approaches on speech data, while the performance of these models on speech is inferior compared to the performance that they achieve on music. Results also include an analysis of false positives, showing that fingerprinting methods are more likely to match with an audio of the same speaker rather than an audio of the same text, when mismatches occur.