A New Benchmark of Aphasia Speech Recognition and Detection Based on
  E-Branchformer and Multi-task Learning
Jiyang Tang William Chen Xuankai Chang Shinji Watanabe Brian MacWhinney
Aphasia is a language disorder that affects the speaking ability of millions
of patients. This paper presents a new benchmark for Aphasia speech recognition
and detection tasks using state-of-the-art speech recognition techniques with
the AphsiaBank dataset. Specifically, we introduce two multi-task learning
methods based on the CTC/Attention architecture to perform both tasks
simultaneously. Our system achieves state-of-the-art speaker-level detection
accuracy (97.3%), and a relative WER reduction of 11% for moderate Aphasia
patients. In addition, we demonstrate the generalizability of our approach by
applying it to another disordered speech database, the DementiaBank Pitt
corpus. We will make our all-in-one recipes and pre-trained model publicly
available to facilitate reproducibility. Our standardized data preprocessing
pipeline and open-source recipes enable researchers to compare results
directly, promoting progress in disordered speech processing.