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Automatic pathological speech assessment

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Speech sound disorder

A speech sound disorder (SSD) is a speech disorder in which some sounds (phonemes) are not produced or used correctly. The term "protracted phonological development" is sometimes preferred when describing children's speech, to emphasize the continuing development while acknowledging the delay. Speech sound disorders may be subdivided into two primary types, articulation disorders (also called phonetic disorders) and phonemic disorders (also called phonological disorders).

Communication disorder

A communication disorder is any disorder that affects an individual's ability to comprehend, detect, or apply language and speech to engage in dialogue effectively with others. The delays and disorders can range from simple sound substitution to the inability to understand or use one's native language. Disorders and tendencies included and excluded under the category of communication disorders may vary by source. For example, the definitions offered by the American Speech–Language–Hearing Association differ from those of the Diagnostic Statistical Manual 4th edition (DSM-IV).

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Perception

Perception () is the organization, identification, and interpretation of sensory information in order to represent and understand the presented information or environment. All perception involves signals that go through the nervous system, which in turn result from physical or chemical stimulation of the sensory system. Vision involves light striking the retina of the eye; smell is mediated by odor molecules; and hearing involves pressure waves.

Meta-learning (computer science)

Meta learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself, hence the alternative term learning to learn.

Speech–language pathology

Speech-language pathology (or speech and language pathology) is a field of healthcare expertise practiced globally. Speech-language pathology (SLP) specializes in the evaluation, diagnosis, treatment, and prevention of communication disorders (speech and language impairments), cognitive-communication disorders, voice disorders, pragmatic disorders, social communication difficulties and swallowing disorder across the lifespan.

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

Supervised learning

Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).

Computer-aided diagnosis

Computer-aided detection (CADe), also called computer-aided diagnosis (CADx), are systems that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, Endoscopy, and ultrasound diagnostics yield a great deal of information that the radiologist or other medical professional has to analyze and evaluate comprehensively in a short time. CAD systems process digital images or videos for typical appearances and to highlight conspicuous sections, such as possible diseases, in order to offer input to support a decision taken by the professional.

Residual neural network

A Residual Neural Network (a.k.a. Residual Network, ResNet) is a deep learning model in which the weight layers learn residual functions with reference to the layer inputs. A Residual Network is a network with skip connections that perform identity mappings, merged with the layer outputs by addition. It behaves like a Highway Network whose gates are opened through strongly positive bias weights. This enables deep learning models with tens or hundreds of layers to train easily and approach better accuracy when going deeper.