Label Noise Robustness and Learning Speed in a Self-Learning Vocal User Interface

Abstract

A self-learning vocal user interface learns to map user-defined spoken commands to intended actions. The voice user interface is trained by mining the speech input and the provoked action on a device. Although this generic procedure allows a great deal of flexibility, it comes at a cost. Two requirements are important to create a user-friendly learning environment. First, the self-learning interface should be robust against typical errors that occur in the interaction between a non-expert user and the system. For instance, the user gives a wrong learning example to the system by commanding “Turn on the television!” and pushing a power button on the wrong remote control. The spoken command is then supervised by a wrong action and we refer to these errors as label noise. Secondly, the mapping between voice commands and intended actions should happen fast, i.e. require few examples. To meet these requirements, we implemented learning through supervised NMF. We tested keyword recognition accuracy for different levels of label noise and different sizes of training sets. Our learning approach is robust against label noise, but some improvement regarding fast mapping is desirable.