This paper presents a fully automated approach for identifying speech anomalies from voice
recordings to aid in the assessment of speech impairments. By combining Connectionist …

Spontaneous speech analysis plays an important role in the study and treatment of aphasia,
but can be difficult to perform manually due to the time consuming nature of speech …

Robust speech recognition is a key prerequisite for semantic feature extraction in automatic
aphasic speech analysis. However, standard one-size-fits-all automatic speech recognition …

In light of medical biases and variability of Aphasia classes, determining the severity of the
Aphasia disorder warrants the use of machine learning to properly leverage patient data for …

Aphasia is a language disorder that affects the speaking ability of millions of patients. This
paper presents a new benchmark for Aphasia speech recognition and detection tasks using …

With the rapid aging of the world's population, the global burden of agingrelated mental
disorders, such as Alzheimer's dementia (AD), is also on the rise. Unfortunately global …

Automatic recognition of aphasic speech is challenging due to various speech-language
impairments associated with aphasia as well as a scarcity of training data appropriate for …

Dementia comes second only to spinal cord injuries in terms of its debilitating effects; from
memory-loss to physical disability. The standard approach to evaluate cognitive conditions …

Aphasia is a type of acquired language impairment resulting from brain injury. Speech
assessment is an important part of the comprehensive assessment process for aphasic …

We employ the method of fine-tuning wav2vec2. 0 for recognition of phonemes in aphasic
speech. Our effort focuses on data augmentation, by supplementing data from both in …