What does the speaker segmentation technology

Speaker segmentation (diarization) is a speech technology allowing you to segment audio (or video) into particular speakers. What is it good for? You can more easily identify speaker turns in a dialog while making speech transcript.

Even if you do not directly need the speaker information, the speaker segmentation is very helpful for speech-to-text technology (STT). The STT technology contains unsupervised speaker adaptation module. This module takes parts of speech belongings to a particular speaker and adapts an acoustic model towards them. Adaptation of the model leads to more accurate speech transcript.

The adaptation - even if it is called speaker adaptation - adapts the system to the whole acoustic channel. It consists of speaker's voice characteristics, room acoustics (echo), microphone characteristics, environment noise, etc.

Speaker segmentation is theoretically independent on speaker, language and acoustic conditions. But - practically - it is dependent. The reason is, that it uses something called a universal background model (UBM). The UBM should model all speech, languages and acoustics of the world - theoretically. But you need to train it on some speaker labeled data - to learn how to distinguish among speakers. And it holds (as in other speech technologies) that the more far the data you process is from the training data, the worse accuracy you get.

Why do we need your speech data

Our several year experience in speech technology research and business shows often clash between:

Speech technology provider: "Give us some of your speech data for testing purposes please."

and

Customer: "No way! Our speech data is our private and secret property."

So let discuss several WHYs.

Why the speech technology provider wants the customer's data?

The speech technologies are very complex and sensitive to match between model and the data. This is common problem in the whole field of machine learning. Once you feed the classifier with "already seen" data, everything goes well. Accuracy of such algorithm is great.
The problem occurs when you put an unseen data into the algorithm - data which was not seen during training and developing. It is like, people living in US understands English because it is their already seen data, but does not understand Japanese because it is their unseen data during the training phase (childhood).