In this work we present a set of techniques which explore information from multiple, different language versions of the same speech, to improve Automatic Speech Recognition (ASR) performance. Using this redundant information we are able to recover acronyms, words that cannot be found in the multiple hypotheses produced by the ASR systems, and pronunciations absent from their pronunciation dictionaries. When used together, the three techniques yield a relative improvement of 5.0% over the WER of our baseline system, and 24.8% relative when compared with standard speech recognition, in an Europarl Committee dataset with three different languages (Portuguese, Spanish and English). One full iteration of the system has a parallel Real Time Factor (RTF) of 3.08 and a sequential RTF of 6.44.
