End-to-end acoustic-articulatory dysarthric speech recognition leveraging large-scale pretrained acoustic features

Abstract

Automatic dysarthric speech recognition (ADSR) remains challenging due to the irregularities in speech caused by motor control impairments and the limited availability of dysarthric speech data. This paper explores the integration of articulatory features, captured using Electromagnetic Articulography (EMA), with both conventional acoustic features and those extracted from large-scale pretrained models including Whisper and XLSR-53 as well as the fine-tuned Whisper model. We propose end-to-end (E2E) Conformer-based acoustic-articulatory models for ADSR and compare their performance against the corresponding hybrid TDNNF models. The experimental results show that using the fine-tuned Whisper features (Whisper-FT) fused with articulatory features achieves the lowest (10.5%) word error rate (WER) on dysarthric speech, with particularly significant improvements for severely dysarthric speech, reaching a WER of 20.8%.