A “hot mike” is one that is transmitting your comments, whether you know the mike is activated or not.
For example, a “hot mike” in 2017 caught this jewel:
Israeli Prime Minister Benjamin Netanyahu called the European Union “crazy” at a private meeting with the leaders of four Central European countries, unaware that a microphone was transmitting his comments to reporters outside.
“The EU is the only association of countries in the world that conditions the relations with Israel, that produces technology and every area, on political conditions. The only ones! Nobody does it. It’s crazy. It’s actually crazy. There is no logic here,” Netanyahu said Wednesday in widely reported remarks.
Netanyahu was meeting with the leaders of Hungary, Slovakia, Czech Republic and Poland, known as the Visegrad Group.
The microphone was switched off after about 15 minutes, according to reports.
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A common aspect of “hot mike” comments is the speaker knew the microphone was present, but assumed it was turned off. In “hot mike” cases, the speaker is known and the relevance of their comments usually obvious.
But what about “non-hot mike” comments? That is comments made by a speaker with no sign of a microphone?
Say casual conversation in a restaurant, at a party, in a taxi, in a conversation at home or work, or anywhere in between?
Laws governing the interception of conversations are vast and complex so before processing any conversation data you suspect to be intercepted, seek legal counsel. This post assumes you have been properly cautioned and chosen to proceed with processing conversation data.
Royal Jain, in Intro to audio processing world for a Data scientist, begins a series of posts to help bridge the gap between NLP and speech/audio processing. Jain writes:
Coming from NLP background I had difficulties in understanding the concepts of speech/audio processing even though a lot of underlying science and concepts were the same. This blog series is an attempt to make the transition easier for people having similar difficulties. The First part of this series describes the feature space which is used by most machine learning/deep learning models.
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Looking forward to more posts in this series!
Data science ethics advocates will quickly point out that privacy concerns surround the interception of private conversations.
They’re right!
But when the privacy in question belows to those who plan, fund and execute regime-change wars, killing hundreds of thousands and making refugees out of millions more, generally increasing human misery on a global scale, I have an answer to the ethics question. My question is one of risk assessment.
You?