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We study the problem of evaluating automatic speech recognition (ASR) systemsthat target dialectal speech input. A major challenge in this case is that theorthography of dialects is typically not standardized. From an ASR evaluationperspective, this means that there is no clear gold standard for the expectedoutput, and several possible outputs could be considered correct according todifferent human annotators, which makes standard word error rate (WER)inadequate as an evaluation metric. Such a situation is typical for machinetranslation (MT), and thus we borrow ideas from an MT evaluation metric, namelyTERp, an extension of translation error rate which is closely-related to WER.In particular, in the process of comparing a hypothesis to a reference, we makeuse of spelling variants for words and phrases, which we mine from Twitter inan unsupervised fashion. Our experiments with evaluating ASR output forEgyptian Arabic, and further manual analysis, show that the resulting WERd(i.e., WER for dialects) metric, a variant of TERp, is more adequate than WERfor evaluating dialectal ASR.

WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition