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

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arXiv · 2305.13331

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

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Abstract

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.

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Jiyang Tang, William Chen, Xuankai Chang, Shinji Watanabe, Brian MacWhinney. 2023-05-19. A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning. https://arxiv.org/abs/2305.13331

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