In this paper, we describe the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge, which asked participants to construct automatic speech recognition systems that were robust to a variety of acoustic environments and recording scenarios without having access to matched training and development data. We discuss the performance of the systems evaluated in the challenge, summarize how those systems were constructed, and draw conclusions about what contributed to the performance levels of the systems on the highly variable, noisy, reverberant evaluation data set constructed for this challenge.
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Mary P. Harper (2015) studied this question.
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