PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mobile Texting: Can Post-ASR Correction Solve the Issues? An Experimental Study on Gain vs. Costs
Michael Feld, Saaedeh Momtazi, Farina Freigang, Dietrich Klakow and Christian Müller
In: IUI 2012, 14 Feb - 17 Feb 2012, Lisbon, Portugal.


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Multimodal Integration
ID Code:8871
Deposited By:Diana Schreyer
Deposited On:21 February 2012