  Despite the rapid progress of automatic speech recognition (ASR) technologies
in the past few decades, recognition of disordered speech remains a highly
challenging task to date. Disordered speech presents a wide spectrum of
challenges to current data intensive deep neural networks (DNNs) based ASR
technologies that predominantly target normal speech. This paper presents
recent research efforts at the Chinese University of Hong Kong (CUHK) to
improve the performance of disordered speech recognition systems on the largest
publicly available UASpeech dysarthric speech corpus. A set of novel modelling
techniques including neural architectural search, data augmentation using
spectra-temporal perturbation, model based speaker adaptation and cross-domain
generation of visual features within an audio-visual speech recognition (AVSR)
system framework were employed to address the above challenges. The combination
of these techniques produced the lowest published word error rate (WER) of
25.21% on the UASpeech test set 16 dysarthric speakers, and an overall WER
reduction of 5.4% absolute (17.6% relative) over the CUHK 2018 dysarthric
speech recognition system featuring a 6-way DNN system combination and cross
adaptation of out-of-domain normal speech data trained systems. Bayesian model
adaptation further allows rapid adaptation to individual dysarthric speakers to
be performed using as little as 3.06 seconds of speech. The efficacy of these
techniques were further demonstrated on a CUDYS Cantonese dysarthric speech
recognition task.
