Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition

The associated neural speech representations produced by these pre-trained Automatic recognition of disordered and elderly speech remains ASR systems are also inherently robust to domain mismatch [24-a highly challenging task to date due to the difficulty in collecting 26]. Although they have been successfully applied to a range of normal such data in large quantities. This paper explores a series of speech processing tasks including speech recognition [21-23, approaches to integrate domain adapted Self-Supervised Learning 27], speech emotion recognition [28] and speaker recognition [29], (SSL) pre-trained models into TDNN and Conformer ASR systems very limited researches on SSL pre-trained models for disordered for dysarthric and elderly speech recognition: a) input feature and elderly speech have been conducted [24, 30, 31]. Among these, fusion between standard acoustic frontends and domain adapted wav2vec2.0