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Articulatory features are inherently invariant to acoustic signal distortionand have been successfully incorporated into automatic speech recognition (ASR)systems designed for normal speech. Their practical application to atypicaltask domains such as elderly and disordered speech across languages is oftenlimited by the difficulty in collecting such specialist data from targetspeakers. This paper presents a cross-domain and cross-lingual A2A inversionapproach that utilizes the parallel audio and ultrasound tongue imaging (UTI)data of the 24-hour TaL corpus in A2A model pre-training before beingcross-domain and cross-lingual adapted to three datasets across two languages:the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech corpora;and the English TORGO dysarthric speech data, to produce UTI based articulatoryfeatures. Experiments conducted on three tasks suggested incorporating thegenerated articulatory features consistently outperformed the baseline TDNN andConformer ASR systems constructed using acoustic features only by statisticallysignificant word or character error rate reductions up to 4.75%, 2.59% and2.07% absolute (14.69%, 10.64% and 22.72% relative) after data augmentation,speaker adaptation and cross system multi-pass decoding were applied.

Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition