To demonstrate how speech recognition application is created, let’s first try to use Pocketsphinx with Python. Python API is really simple, example is just six lines of code.

In the meantime, Facebook is releasing the code for Wav2vec-U in open source to enable developers to build speech recognition systems using unlabeled speech audio recordings and unlabeled text.

A source previously told me that years ago, Facebook was interested in developing its own speech recognition software designed specifically to accurately transcribe how friends talk to each other.

Facebook has developed a machine learning system that can recognize speech without the need for transcribed data.

But with automated speech recognition programs, there is no code-switching—either you assimilate, or you are not understood.

Usually this would get passed to some kind of virtual assistant software, but in this case, a Python script breaks the speech down into individual characters and looks up their Morse representations.

In a study, researchers found the error rates of several commercial speech recognition models to be higher than previously assumed.