Disordered speech recognition considering low resources and abnormal articulation

The success of automatic speech recognition (ASR) benefits a great number of healthy people, but not people with disorders. The speech disordered may truly need support from technology, while they actually gain little. The difficulties of disordered ASR arise from the limited availability of data and the abnormal nature of speech, e.g, unclear, unstable, and incorrect pronunciations. To realize the ASR of disordered speech, this study addresses the problems of disordered speech in two respects, low resources, and articulatory abnormality. In order to solve the problem of low resources, this study proposes staged knowledge distillation (KD), which provides different references to the student models according to their mastery of knowledge, so as to avoid feature overfitting. To tackle the articulatory abnormalities in dysarthria, we propose an intended phonological perception method (IPPM) by applying the motor theory of speech perception to ASR, in which pieces of intended phonological features are estimated and provided to ASR. And further, we solve the challenges of disordered ASR by combining the staged KD and the IPPM. TORGO database and UASEECH corpus are two commonly used datasets of dysarthria which is the main cause of speech disorders. Experiments on the two datasets validated the effectiveness of the proposed methods. Compared with the baseline, the proposed method achieves 35.14%$\sim$38.12% relative phoneme error rate reductions (PERRs) for speakers with varying degrees of dysarthria on the TORGO database and relative 8.17%$\sim$13.00% PERRs on the UASPEECH corpus. The experiments demonstrated that addressing disordered speech from both low resources and speech abnormality is an effective way to solve the problems, and the proposed methods significantly improved the performance of ASR for disordered speech.