Investigating the Sensitivity of Automatic Speech Recognition Systems to
  Phonetic Variation in L2 Englishes
Emma O'Neill Julie Carson-Berndsen
Automatic Speech Recognition (ASR) systems exhibit the best performance on
speech that is similar to that on which it was trained. As such,
underrepresented varieties including regional dialects, minority-speakers, and
low-resource languages, see much higher word error rates (WERs) than those
varieties seen as 'prestigious', 'mainstream', or 'standard'. This can act as a
barrier to incorporating ASR technology into the annotation process for
large-scale linguistic research since the manual correction of the erroneous
automated transcripts can be just as time and resource consuming as manual
transcriptions. A deeper understanding of the behaviour of an ASR system is
thus beneficial from a speech technology standpoint, in terms of improving ASR
accuracy, and from an annotation standpoint, where knowing the likely errors
made by an ASR system can aid in this manual correction. This work demonstrates
a method of probing an ASR system to discover how it handles phonetic variation
across a number of L2 Englishes. Specifically, how particular phonetic
realisations which were rare or absent in the system's training data can lead
to phoneme level misrecognitions and contribute to higher WERs. It is
demonstrated that the behaviour of the ASR is systematic and consistent across
speakers with similar spoken varieties (in this case the same L1) and phoneme
substitution errors are typically in agreement with human annotators. By
identifying problematic productions specific weaknesses can be addressed by
sourcing such realisations for training and fine-tuning thus making the system
more robust to pronunciation variation.