Dysarthria

About: Dysarthria is a research topic. Over the lifetime, 2402 publications have been published within this topic receiving 56554 citations.

Assessing Virtual Assistant Capabilities with Italian Dysarthric Speech

Abstract: The usage of smartphone-based virtual assistants (e.g., Siri or Google Assistant) is growing, and their spread has generally a positive impact on device accessibility, e.g., for people with disabilities. However, people with dysarthria or other speech impairments may be unable to use these virtual assistants with proficiency. This paper investigates to which extent people with ALS-induced dysarthria can be understood and get consistent answers by three widely used smartphone-based assistants, namely Siri, Google Assistant, and Cortana. We focus on the recognition of Italian dysarthric speech, to study the behavior of the virtual assistants with this specific population for which no relevant studies are available. We collected and recorded suitable speech samples from people with dysarthria in a dedicated center of the Molinette hospital, in Turin, Italy. Starting from those recordings, the differences between such assistants, in terms of speech recognition and consistency in answer, are investigated and discussed. Results highlight different performance among the virtual assistants. For speech recognition, Google Assistant is the most promising, with around 25% of word error rate per sentence. Consistency in answer, instead, sees Siri and Google Assistant provide coherent answers around 60% of times.

Crosslinguistic application of English-centric rhythm descriptors in motor speech disorders.

Abstract: Background: Rhythmic disturbances are a hallmark of motor speech disorders, in which the motor control deficits interfere with the outward flow of speech and by extension speech understanding. As the functions of rhythm are language-specific, breakdowns in rhythm should have language-specific consequences for communication. Objective: The goals of this paper are to (i) provide a review of the cognitive-linguistic role of rhythm in speech perception in a general sense and crosslinguistically; (ii) present new results of lexical segmentation challenges posed by different types of dysarthria in American English, and (iii) offer a framework for crosslinguistic considerations for speech rhythm disturbances in the diagnosis and treatment of communication disorders associated with motor speech disorders. Summary: This review presents theoretical and empirical reasons for considering speech rhythm as a critical component of communication deficits in motor speech disorders, and addresses the need for crosslinguistic research to explore languageuniversal versus language-specific aspects of motor speech disorders.

Production knowledge in the recognition of dysarthric speech

Abstract: Millions of individuals have acquired or have been born with neuro-motor conditions that limit the control of their muscles, including those that manipulate the articulators of the vocal tract. These conditions, collectively called dysarthria, result in speech that is very difficult to understand, despite being generally syntactically and semantically correct. This difficulty is not limited to human listeners, but also adversely affects the performance of traditional automatic speech recognition (ASR) systems, which in some cases can be completely unusable by the affected individual. This dissertation describes research into improving ASR for speakers with dysarthria by means of incorporated knowledge of their speech production. The document first introduces theoretical aspects of dysarthria and of speech production and outlines related work in these combined areas within ASR. It then describes the acquisition and analysis of the TORGO database of dysarthric articulatory motion and demonstrates several consistent behaviours among speakers in this database, including predictable pronunciation errors, for example. Articulatory data are then used to train augmented ASR systems that model the statistical relationships between vocal tract configurations and their acoustic consequences. I show that dynamic Bayesian networks augmented with instantaneous theoretical or empirical articulatory variables outperform even discriminative alternatives. This leads to work that incorporates a more rigid theory of speech production, i.e., task-dynamics, that models the high-level and long-term aspects of speech production. For this task, I devised an algorithm for estimating articulatory positions given only acoustics that significantly outperforms the state-of-the-art. Finally, I present ongoing work into the transformation and re-synthesis of dysarthric speech in order to make it more intelligible to human listeners. This research represents definitive progress towards the accommodation of dysarthric speech within modern speech recognition systems. However, there is much more research that remains to be undertaken and I conclude with some thoughts as to which paths we might now take.

The Effect of Three Practice Conditions on the Consistency of Chronic Dysarthric Speech

Abstract: This study investigated whether it is possible for people with chronic dysarthria to adjust their articulation in three practice conditions. A speaker dependent, speech recognition system was used to compare participants' practice attempts with a model of a word made from previous recordings to give a recognition score. This score was used to indicate changes in production of practice words with different conditions. The three conditions were reading of written target words, visual feedback, and an auditory model followed by visual feedback. For eight participants with dysarthria, the ability to alter speech production was shown, together with a differential effect of the three conditions. Copying an auditory target gave significantly better recognition scores than just repeating the word. Visual feedback was no more effective than repetition alone. For four control participants, visual feedback did produce significantly better recognition scores than just repetition of written words, and the presence of an auditory model was Significantly more effective than visual feedback. Possible reasons for differences between conditions are discussed.

End-to-end Dysarthric Speech Recognition Using Multiple Databases

Abstract: We present in this paper an end-to-end automatic speech recognition (ASR) system for a person with an articulation disorder resulting from athetoid cerebral palsy. In the case of a person with this type of articulation disorder, the speech style is quite different from that of a physically unimpaired person, and the amount of their speech data available to train the model is limited because their burden is large due to strain on the speech muscles. Therefore, the performance of ASR systems for people with an articulation disorder degrades significantly. In this paper, we propose an end-to-end ASR framework trained by not only the speech data of a Japanese person with an articulation disorder but also the speech data of a physically unimpaired Japanese person and a non-Japanese person with an articulation disorder to relieve the lack of training data of a target speaker. An end-to-end ASR model encapsulates an acoustic and language model jointly. In our proposed model, an acoustic model portion is shared between persons with dysarthria, and a language model portion is assigned to each language regardless of dysarthria. Experimental results show the merit of our proposed approach of using multiple databases for speech recognition.