However, your description of what it would need to work is woefully naïve. There were startup funded apps that matched entire recorded song libraries to ambient sound (along with location) in the age of flip phones to track copyright violations in clubs. They relied on matching compressed periodogram hashes with an error tolerant database. The databases were large, but not unwieldy, and the computational load reasonable in the early 2000s. The ability to craft a database to a person to match a large set of commercial words/names with very low bandwidth is not impossible. Machine learning has made it easier.