This is hardly a new concept btw.
In 2005 ACM's CCS Zhuang, Zhou and Tygar presented Keyboard Acoustic Emanations Revisited [1]
We examine the problem of keyboard acoustic emanations. We
present a novel attack taking as input a 10-minute sound recording
of a user typing English text using a keyboard, and then recovering
up to 96% of typed characters. There is no need for a labeled
training recording. Moreover the recognizer bootstrapped this way
can even recognize random text such as passwords: In our experiments,
90% of 5-character random passwords using only letters can
be generated in fewer than 20 attempts by an adversary; 80% of 10-
character passwords can be generated in fewer than 75 attempts.
Our attack uses the statistical constraints of the underlying content,
English language, to reconstruct text from sound recordings
without any labeled training data. The attack uses a combination
of standard machine learning and speech recognition techniques,
including cepstrum features, Hidden Markov Models, linear classification,
and feedback-based incremental learning
which builds up on Asonov & Agrawal's work [2] who came up with the idea the previous year (2004).
We show that PC keyboards, notebook keyboards, telephone
and ATM pads are vulnerable to attacks based on
differentiating the sound emanated by different keys. Our
attack employs a neural network to recognize the key being
pressed. We also investigate why different keys produce
different sounds and provide hints for the design of homophonic
keyboards that would be resistant to this type of attack.
[1]
https://dl.acm.org/doi/10.1145/1609956.1609959[2] https://ieeexplore.ieee.org/document/1301311