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 · 2023
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 Aphsia-Bank dataset.Specifically, we introduce two multi-task learning methods based on the CTC/Attention architecture to perform both tasks simultaneously.Our system achieves state-ofthe-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 Demen-tiaBank 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.