Abstract The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies. One prominent clinical application is a computer-assisted speech training system which enables personalized speech therapy to patients impaired by communicative disorders in the patient’s home environment. Such a system relies on the robust automatic speech recognition (ASR) technology to be able to provide accurate articulation feedback. With the long-term aim of developing off-the-shelf ASR systems that can be incorporated in clinical context without prior speaker information, we compare the ASR performance of speaker-independent bottleneck and articulatory features on dysarthric speech used in conjunction with dedicated neural network-based acoustic models that have been shown to be robust against spectrotemporal deviations. We report ASR performance of these systems on two dysarthric speech datasets of different characteristics to quantify the achieved performance gains. Despite the remaining performance gap between the dysarthric and normal speech, significant improvements have been reported on both datasets using speaker-independent ASR architectures.
In this paper, we investigate the recognition of speech uttered by a person with an articulation disorder resulting from athetoid cerebral palsy based on a robust feature extraction method using pre-trained convolutive bottleneck networks (CBN). Generally speaking, the amount of speech data obtained from a person with an articulation disorder is limited because their burden is large due to strain on the speech muscles. Therefore, a trained CBN tends toward overfitting for a small corpus of training data. In our previous work, the experimental results showed speech recognition using features extracted from CBNs outperformed conventional features. However, the recognition accuracy strongly depends on the initial values of the convolution kernels. To prevent overfitting in the networks, we introduce in this paper a pre-training technique using a convolutional restricted Boltzmann machine (CRBM). Through word-recognition experiments, we confirmed its superiority in comparison to convolutional networks without pre-training.  This paper describes the application of state-of-the-art automatic speech recognition (ASR) systems to objective assessment of voice and speech disorders. Acoustical analysis of speech has long been considered a promising approach to non-invasive and objective assessment of people. In the past the types and amount of speech materials used for acoustical assessment were very limited. With the ASR technology, we are able to perform acoustical and linguistic analyses with a large amount of natural speech from impaired speakers. The present study is focused on Cantonese, which is a major Chinese dialect. Two representative disorders of speech production are investigated: dysphonia and aphasia. ASR experiments are carried out with continuous and spontaneous speech utterances from Cantonese-speaking patients. The results confirm the feasibility and potential of using natural speech for acoustical assessment of voice and speech disorders, and reveal the challenging issues in acoustic modeling and language modeling of pathological speech.