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.

本文介绍了中国香港中文大学对自动语音识别技术中有关于不正常发音语音的识别问题展开的最新研究成果，利用了新型建模技术和 Bayesian model adaptation 等方法，最终取得了乌阿里语（UASpeech）失语症语音库上最低的 25.21% 字误率（WER），并在多方面相对于 2018 年的同类系统均有较大提升