Post-Stroke Speech Transcription Challenge (Task B): Correctness Detection in Anomia Diagnosis with Imperfect Transcripts

Aphasia is a language disorder that affects millions of adults worldwide annually; it is most commonly caused by strokes or neurodegenerative diseases. Anomia, or word finding difficulty, is a prominent symptom of aphasia, which is often diagnosed through confrontation naming tasks. In the clinical setting, identification of correctness in responses to these naming tasks is useful for diagnosis, but currently is a labor-intensive process. This year’s Post-Stroke Speech Transcription Challenge provides an opportunity to explore ways of automating this process. In this work, we focus on Task B of the challenge, i.e. identification of response correctness. We study whether a simple aggregation of using the 1-best automatic speech recognition (ASR) output and acoustic features could help predict response correctness. This was motivated by the hypothesis that acoustic features could provide complementary information to the (imperfect) ASR transcripts. We trained several classifiers using various sets of acoustic features standard in speech processing literature in an attempt to improve over the 1-best ASR baseline. Results indicated that our approach to using the acoustic features did not beat the simple baseline, at least on this challenge dataset. This suggests that ASR robustness still plays a significant role in the correctness detection task, which has yet to benefit from acoustic features.