Parakeet: a continuous speech recognition system for mobile touch-screen devices
Proceedings of the 14th international conference on Intelligent user interfaces
Making use of drivers' glances onto the screen for explicit gaze-based interaction
Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications
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The next big step in embedded, mobile speech recognition will be to allow completely free input as it is needed for messaging like SMS or email. However, unconstrained dictation remains error-prone, especially when the environment is noisy. In this paper, we compare different methods for improving a given free-text dictation system used to enter textbased messages in embedded mobile scenarios, where distraction, interaction cost, and hardware limitations enforce strict constraints over traditional scenarios. We present a corpus-based evaluation, measuring the trade-off between improvement of the word error rate versus the interaction steps that are required under various parameters. Results show that by post-processing the output of a "black box" speech recognizer (e.g. a web-based speech recognition service), a reduction of word error rate by 55% (10.3% abs.) can be obtained. For further error reduction, however, a richer representation of the original hypotheses (e.g. lattice) is necessary.