This paper presents a probabilistic framework that combines multiple knowledge sources for Haptic Voice Recognition (HVR), a multi-modal input method designed to provide efficient text entry on modern mobile devices. HVR extends the conventional voice input by allowing users to provide complementary partial lexical information via touch input to improve the efficiency and accuracy of voice recognition. This paper investigates the use of the initial letter of the words in the utterance as the partial lexical information. In addition to the acoustic and language models used in automatic speech recognition systems, HVR uses the haptic and partial lexical models as additional knowledge sources to reduce the recognition search space and suppress confusions. Experimental results show that both the word error rate and runtime factor can be reduced by a factor of two using HVR.
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