  Automatic speech recognition (ASR) is a relevant area in multiple settings
because it provides a natural communication mechanism between applications and
users. ASRs often fail in environments that use language specific to particular
application domains. Some strategies have been explored to reduce errors in
closed ASRs through post-processing, particularly automatic spell checking, and
deep learning approaches. In this article, we explore using a deep neural
network to refine the results of a phonetic correction algorithm applied to a
telesales audio database. The results exhibit a reduction in the word error
rate (WER), both in the original transcription and in the phonetic correction,
which shows the viability of deep learning models together with post-processing
correction strategies to reduce errors made by closed ASRs in specific language
domains.
