Such methods are often combined with missing feature approaches, which use a single -
talker model to recognize the speech based on regions of the spectrogram that are dominated by the target talker, while ignoring areas dominated by other sounds.
The n -
talker model must therefore handle 10,000 n possible sound combinations in each frame.
For example, if there are six speakers, each with 10,000 sounds, each iteration must evaluate 60,000 sound combinations — a lot less than the trillion trillion combinations in the full six -
talker model!
Not exact matches
Being a smooth
talker to land clients without first validating your business
model is a recipe for disaster.
Yet a critically important challenge remained: to find an algorithm that would scale up and remain efficient when dealing with larger speech
models and more
talkers.
A brute force approach to analyze this overlapping speech is to combine two single -
talker speech
models to form a two - speaker
model.
Loopy belief propagation Our algorithm, like those of many groups, uses the top - down approach, which tries to
model the full sound from multiple
talkers without first picking out special regions in the spectrogram.