Based on a recent language center and departmental implementation (see e.g. Workshop), this paper will give a brief overview over the current research status of the (known "hard task") of speech recognition as a prominent subset of the (recently much more widely discussed) automated Natural Language Processing, compare various engineering implementations that are more or less readily available to the end user (MS-Windows, Google (Chrome/Android), language learning material providers like Auralog), and discuss their possible application in SLA programs for speaking practice in various dictation exercises (including recognition samples from native speakers and language learners) and integration into language learner achievement ePortfolios.
