Fast word acquisition in an NMF-based learning framework

Abstract

A speech recognition system that automatically learns word models for a small vocabulary from examples of its usage, without using prior linguistic information, can be of great use in cognitive robotics, human-machine interfaces, and assistive devices. In the latter case, the user's speech capabilities may also be affected. In this paper, we consider a NMF-based learning framework capable of doing this, and experimentally show that its learning rate crucially depends on how the speech data is represented. Higher-level units of speech, which hide some of the complex variability of the acoustics, are found to yield faster learning rates.